데마_xgboost.ipynb

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    "colab": {
      "name": "데마_xgboost",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "cell_type": "code",
      "metadata": {
        "id": "wphcOiU01Hko",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "7dda6e70-10de-40fd-cce7-a7512b3d13c0"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from scipy import interp\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "from matplotlib import pyplot\n",
        "from sklearn.model_selection import StratifiedKFold, GridSearchCV,train_test_split\n",
        "from sklearn.metrics import roc_curve, precision_recall_curve, auc, make_scorer, recall_score, accuracy_score, precision_score, confusion_matrix\n",
        "import xgboost as xgb\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrCXEKh42C8d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "root='/content/drive/My Drive/data.csv'\n",
        "df=pd.read_csv(root)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wc-_QMX2bH5t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "a60df817-63d4-415e-c512-36164d0763d7"
      },
      "source": [
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
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              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
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              "      <td>0.08902</td>\n",
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              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
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              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
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              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
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              "      <td>0.01867</td>\n",
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              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
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              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
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              "      <td>22.540</td>\n",
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              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0      842302         M  ...                  0.11890          NaN\n",
              "1      842517         M  ...                  0.08902          NaN\n",
              "2    84300903         M  ...                  0.08758          NaN\n",
              "3    84348301         M  ...                  0.17300          NaN\n",
              "4    84358402         M  ...                  0.07678          NaN\n",
              "..        ...       ...  ...                      ...          ...\n",
              "564    926424         M  ...                  0.07115          NaN\n",
              "565    926682         M  ...                  0.06637          NaN\n",
              "566    926954         M  ...                  0.07820          NaN\n",
              "567    927241         M  ...                  0.12400          NaN\n",
              "568     92751         B  ...                  0.07039          NaN\n",
              "\n",
              "[569 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFmU_Mop2P0O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "24f7627e-8512-4a56-b6b2-b06645f2b5c8"
      },
      "source": [
        "df['diagnosis'].value_counts()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7erdh1aRPQDi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = df.diagnosis                          # M or B \n",
        "x = df.iloc[:,2:32]\n",
        "data = x\n",
        "data_n_2 = (data - data.mean()) / (data.std())              # standardization"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZgwoOEsXmli",
        "colab_type": "text"
      },
      "source": [
        "data visualization은 어차피 동일한 형태일테니 xgboost 부분에서만 나타내었다. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvqL7th0Qten",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "outputId": "116d5452-0e09-48f0-a0e3-f1dfc600c90f"
      },
      "source": [
        "# mean에 관한 data visualization\n",
        "data = pd.concat([y,data_n_2.iloc[:,0:10]],axis=1)\n",
        "data = pd.melt(data,id_vars=\"diagnosis\",\n",
        "                    var_name=\"features\",\n",
        "                    value_name='value')\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.violinplot(x=\"features\", y=\"value\", hue=\"diagnosis\", data=data,split=True, inner=\"quart\")\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
              " <a list of 10 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnleiDyeijP3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "outputId": "a58cfd6f-4dbb-4207-99ab-431ebea3e35f"
      },
      "source": [
        "data = pd.concat([y,data_n_2.iloc[:,10:20]],axis=1)\n",
        "data = pd.melt(data,id_vars=\"diagnosis\",\n",
        "                    var_name=\"features\",\n",
        "                    value_name='value')\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.violinplot(x=\"features\", y=\"value\", hue=\"diagnosis\", data=data,split=True, inner=\"quart\")\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
              " <a list of 10 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bo-VNPNTZIb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "outputId": "179b943d-a5cd-4e2a-c2ec-77d947a4e601"
      },
      "source": [
        "#worst에 관한 data visualization\n",
        "data = pd.concat([y,data_n_2.iloc[:,20:31]],axis=1)\n",
        "data = pd.melt(data,id_vars=\"diagnosis\",\n",
        "                    var_name=\"features\",\n",
        "                    value_name='value')\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.violinplot(x=\"features\", y=\"value\", hue=\"diagnosis\", data=data,split=True, inner=\"quart\")\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
              " <a list of 10 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gapDFlAj2R5f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "41f6345e-32c3-4af9-f759-6e2deb5328b4"
      },
      "source": [
        "lb = LabelBinarizer()\n",
        "df['diagnosis'] = lb.fit_transform(df['diagnosis'].values)\n",
        "targets=df['diagnosis']\n",
        "y=df['diagnosis']\n",
        "df.drop(['id', 'diagnosis', 'Unnamed: 32'], axis=1, inplace=True)\n",
        "\n",
        "targets"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "564    1\n",
              "565    1\n",
              "566    1\n",
              "567    1\n",
              "568    0\n",
              "Name: diagnosis, Length: 569, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpxyvyi9Bfzw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,stratify=y,test_size=0.1,random_state=4)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "le1e0kfD4C_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = xgb.XGBClassifier() #!!!!!!!!  분류기 !!!!!!!!!\n",
        "cv = StratifiedKFold(n_splits=10,shuffle=False)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lwq9XSf-FaSj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "outputId": "d8e6231c-c50f-410e-ada1-f9c1015d1e49"
      },
      "source": [
        "clf.fit(x_train,y_train)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4neLyAQEYCY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=clf.predict(x_test)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFFot6JbD0lq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "c76a53df-1e7f-4976-a079-2ec836796a89"
      },
      "source": [
        "feature_important =clf.fit(x_train, y_train).get_booster().get_fscore()\n",
        "data = pd.DataFrame(data=feature_important.values(), index=feature_important.keys(), columns=[\"score\"]).sort_values(by = \"score\", ascending=False)\n",
        "print(data[:8])\n",
        "xgb.plot_importance(clf)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                      score\n",
            "texture_worst            54\n",
            "texture_mean             42\n",
            "area_se                  40\n",
            "area_worst               39\n",
            "concave points_worst     34\n",
            "smoothness_worst         33\n",
            "concavity_worst          33\n",
            "concave points_mean      27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f150bea58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6liG4u9DTyq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "7e439962-6b99-40b2-c772-28ce84ab1c09"
      },
      "source": [
        "confusion_matrix(y_test,clf.predict(x_test))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[34,  2],\n",
              "       [ 3, 18]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuUnIMElDyfK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "4f94ae59-012b-4995-b074-6d4ed8c4adbc"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.94      0.93        36\n",
            "           1       0.90      0.86      0.88        21\n",
            "\n",
            "    accuracy                           0.91        57\n",
            "   macro avg       0.91      0.90      0.90        57\n",
            "weighted avg       0.91      0.91      0.91        57\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waDcqAQmDca2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "99febd7f-b0e6-424a-fc40-c1ce46190a49"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "from sklearn import metrics\n",
        "\n",
        "fpr, tpr, thresholds = metrics.roc_curve(y_test, clf.predict_proba(x_test)[:,1], pos_label=1)\n",
        "roc_auc = metrics.auc(fpr, tpr)\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic example')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxFKVehzGe9V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "5f30c1e5-18f3-4ae4-d614-97d8cf79c1f0"
      },
      "source": [
        "accuracy_score(y_test,clf.predict(x_test))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9122807017543859"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLxnzg_ZPo3q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "fd9b20c9-14d6-46ed-bb42-135044c33deb"
      },
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.model_selection import cross_val_score\n",
        "cvs=cross_val_score(clf, df, targets, cv=cv)\n",
        "print(cvs)\n",
        "mean_acc=np.mean(cvs)\n",
        "mean_acc"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.         0.9122807  0.94736842 0.94736842 0.98245614 1.\n",
            " 0.94736842 0.98245614 0.94736842 0.98214286]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9648809523809524"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o4QrJvpeb1rZ",
        "colab_type": "text"
      },
      "source": [
        "정밀도(precision)은 양성 클래스에 속한다고 출력한 샘플 중 실제로 양성 클래스에 속하는 샘플 수의 비율을 말한다.  재현율(recall)은 실제 양성 클래스에 속한 표본 중에 양성 클래스에 속한다고 출력한 표본의 수의 비율을 뜻한다. 높을수록 좋은 모형이다. F1-score은 precision과 recall의 밸런스를 측정해준다. Support는 각각의 class가 y_train에서 일어난 수이다. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVSNKSJ_4IbL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "25fa318f-a390-41f2-8495-441326316b24"
      },
      "source": [
        "tprs = []\n",
        "aucs = []\n",
        "\n",
        "mean_fpr = np.linspace(0,1,100) # 기본적으로 배열을 0에서 1사이의 값을 100등분한 것으로 채워라!\n",
        "i = 1\n",
        "for train,test in cv.split(df,targets):\n",
        "    prob=clf.fit(df.iloc[train], targets.iloc[train]).predict_proba(df.iloc[test])[:,1] #실제 positive에 대한 prob를 구해서 prob으로 넣어줘야함...ㅠㅠ\n",
        "    y_pred=clf.predict(df.iloc[test]) # k-fold를 진행할 때 마다의 y_test에 대한 prediction\n",
        "    c_mat=confusion_matrix(targets[test],y_pred)\n",
        "    print(accuracy_score(targets[test],y_pred))\n",
        "    print(c_mat)\n",
        "    print(classification_report(targets[test],y_pred))\n",
        "    for k in range(len(targets[test])):\n",
        "      if targets[test].iloc[k]==y_pred[k]: \n",
        "        #여기서 ==라고 하면 예측과 실제가 맞는 것의 확률, 특성이 나오고 =!로 하면 예측과 실제가 맞지 않는 것의 확률, 특성이 나옴\n",
        "          if y_pred[k] == 1 and prob[k]>0.999: #확률에 따라 출력하고 싶은 값들을 조절할 수 있다.\n",
        "           print(prob[k])\n",
        "           print(\"실제 lable : \")\n",
        "           print(targets[test].iloc[k])\n",
        "           print(\"예측 : \")\n",
        "           print(y_pred[k])         \n",
        "           print(df.iloc[test].iloc[k,:])\n",
        "      elif y_pred[k]==0 and 1-prob[k]>0.999:\n",
        "         print(1-prob[k])\n",
        "         print(\"실제 lable : \")\n",
        "         print(targets[test].iloc[k])\n",
        "         print(\"예측 : \")\n",
        "         print(y_pred[k]) \n",
        "         print(df.iloc[test].iloc[k,:])\n",
        "    fpr, tpr, t = roc_curve(targets[test],prob)\n",
        "    tprs.append(interp(mean_fpr, fpr, tpr))\n",
        "    roc_auc = auc(fpr, tpr)\n",
        "    aucs.append(roc_auc)\n",
        "    feature_important =clf.fit(df.iloc[train], targets.iloc[train]).get_booster().get_fscore()\n",
        "    data = pd.DataFrame(data=feature_important.values(), index=feature_important.keys(), columns=[\"score\"]).sort_values(by = \"score\", ascending=False)\n",
        "    print(data[:8])\n",
        "\n",
        "\n",
        "    plt.plot(fpr, tpr,lw=2, alpha=0.3, label=r'ROC fold %d (AUC = %0.3f)' % (i, roc_auc))\n",
        "    plt.legend(loc=\"lower right\")\n",
        "    i= i+1\n",
        "\n",
        "\n",
        "plt.plot([0,1],[0,1],linestyle = '--',lw = 2,color = 'black')\n",
        "mean_tpr = np.mean(tprs, axis=0)\n",
        "mean_auc = auc(mean_fpr, mean_tpr)\n",
        "plt.plot(mean_fpr, mean_tpr, color='red',\n",
        "         label=r'Mean ROC (AUC = %0.3f )' % (mean_auc),alpha=1)\n",
        "\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC')\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n",
            "[[35  0]\n",
            " [ 0 22]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        35\n",
            "           1       1.00      1.00      1.00        22\n",
            "\n",
            "    accuracy                           1.00        57\n",
            "   macro avg       1.00      1.00      1.00        57\n",
            "weighted avg       1.00      1.00      1.00        57\n",
            "\n",
            "0.9990681\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  18.250000\n",
            "texture_mean                 19.980000\n",
            "perimeter_mean              119.600000\n",
            "area_mean                  1040.000000\n",
            "smoothness_mean               0.094630\n",
            "compactness_mean              0.109000\n",
            "concavity_mean                0.112700\n",
            "concave points_mean           0.074000\n",
            "symmetry_mean                 0.179400\n",
            "fractal_dimension_mean        0.057420\n",
            "radius_se                     0.446700\n",
            "texture_se                    0.773200\n",
            "perimeter_se                  3.180000\n",
            "area_se                      53.910000\n",
            "smoothness_se                 0.004314\n",
            "compactness_se                0.013820\n",
            "concavity_se                  0.022540\n",
            "concave points_se             0.010390\n",
            "symmetry_se                   0.013690\n",
            "fractal_dimension_se          0.002179\n",
            "radius_worst                 22.880000\n",
            "texture_worst                27.660000\n",
            "perimeter_worst             153.200000\n",
            "area_worst                 1606.000000\n",
            "smoothness_worst              0.144200\n",
            "compactness_worst             0.257600\n",
            "concavity_worst               0.378400\n",
            "concave points_worst          0.193200\n",
            "symmetry_worst                0.306300\n",
            "fractal_dimension_worst       0.083680\n",
            "Name: 6, dtype: float64\n",
            "0.99910504\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  16.130000\n",
            "texture_mean                 20.680000\n",
            "perimeter_mean              108.100000\n",
            "area_mean                   798.800000\n",
            "smoothness_mean               0.117000\n",
            "compactness_mean              0.202200\n",
            "concavity_mean                0.172200\n",
            "concave points_mean           0.102800\n",
            "symmetry_mean                 0.216400\n",
            "fractal_dimension_mean        0.073560\n",
            "radius_se                     0.569200\n",
            "texture_se                    1.073000\n",
            "perimeter_se                  3.854000\n",
            "area_se                      54.180000\n",
            "smoothness_se                 0.007026\n",
            "compactness_se                0.025010\n",
            "concavity_se                  0.031880\n",
            "concave points_se             0.012970\n",
            "symmetry_se                   0.016890\n",
            "fractal_dimension_se          0.004142\n",
            "radius_worst                 20.960000\n",
            "texture_worst                31.480000\n",
            "perimeter_worst             136.800000\n",
            "area_worst                 1315.000000\n",
            "smoothness_worst              0.178900\n",
            "compactness_worst             0.423300\n",
            "concavity_worst               0.478400\n",
            "concave points_worst          0.207300\n",
            "symmetry_worst                0.370600\n",
            "fractal_dimension_worst       0.114200\n",
            "Name: 17, dtype: float64\n",
            "0.99929595\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  19.810000\n",
            "texture_mean                 22.150000\n",
            "perimeter_mean              130.000000\n",
            "area_mean                  1260.000000\n",
            "smoothness_mean               0.098310\n",
            "compactness_mean              0.102700\n",
            "concavity_mean                0.147900\n",
            "concave points_mean           0.094980\n",
            "symmetry_mean                 0.158200\n",
            "fractal_dimension_mean        0.053950\n",
            "radius_se                     0.758200\n",
            "texture_se                    1.017000\n",
            "perimeter_se                  5.865000\n",
            "area_se                     112.400000\n",
            "smoothness_se                 0.006494\n",
            "compactness_se                0.018930\n",
            "concavity_se                  0.033910\n",
            "concave points_se             0.015210\n",
            "symmetry_se                   0.013560\n",
            "fractal_dimension_se          0.001997\n",
            "radius_worst                 27.320000\n",
            "texture_worst                30.880000\n",
            "perimeter_worst             186.800000\n",
            "area_worst                 2398.000000\n",
            "smoothness_worst              0.151200\n",
            "compactness_worst             0.315000\n",
            "concavity_worst               0.537200\n",
            "concave points_worst          0.238800\n",
            "symmetry_worst                0.276800\n",
            "fractal_dimension_worst       0.076150\n",
            "Name: 18, dtype: float64\n",
            "0.99933225\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  21.160000\n",
            "texture_mean                 23.040000\n",
            "perimeter_mean              137.200000\n",
            "area_mean                  1404.000000\n",
            "smoothness_mean               0.094280\n",
            "compactness_mean              0.102200\n",
            "concavity_mean                0.109700\n",
            "concave points_mean           0.086320\n",
            "symmetry_mean                 0.176900\n",
            "fractal_dimension_mean        0.052780\n",
            "radius_se                     0.691700\n",
            "texture_se                    1.127000\n",
            "perimeter_se                  4.303000\n",
            "area_se                      93.990000\n",
            "smoothness_se                 0.004728\n",
            "compactness_se                0.012590\n",
            "concavity_se                  0.017150\n",
            "concave points_se             0.010380\n",
            "symmetry_se                   0.010830\n",
            "fractal_dimension_se          0.001987\n",
            "radius_worst                 29.170000\n",
            "texture_worst                35.590000\n",
            "perimeter_worst             188.000000\n",
            "area_worst                 2615.000000\n",
            "smoothness_worst              0.140100\n",
            "compactness_worst             0.260000\n",
            "concavity_worst               0.315500\n",
            "concave points_worst          0.200900\n",
            "symmetry_worst                0.282200\n",
            "fractal_dimension_worst       0.075260\n",
            "Name: 23, dtype: float64\n",
            "0.99937856\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  16.650000\n",
            "texture_mean                 21.380000\n",
            "perimeter_mean              110.000000\n",
            "area_mean                   904.600000\n",
            "smoothness_mean               0.112100\n",
            "compactness_mean              0.145700\n",
            "concavity_mean                0.152500\n",
            "concave points_mean           0.091700\n",
            "symmetry_mean                 0.199500\n",
            "fractal_dimension_mean        0.063300\n",
            "radius_se                     0.806800\n",
            "texture_se                    0.901700\n",
            "perimeter_se                  5.455000\n",
            "area_se                     102.600000\n",
            "smoothness_se                 0.006048\n",
            "compactness_se                0.018820\n",
            "concavity_se                  0.027410\n",
            "concave points_se             0.011300\n",
            "symmetry_se                   0.014680\n",
            "fractal_dimension_se          0.002801\n",
            "radius_worst                 26.460000\n",
            "texture_worst                31.560000\n",
            "perimeter_worst             177.000000\n",
            "area_worst                 2215.000000\n",
            "smoothness_worst              0.180500\n",
            "compactness_worst             0.357800\n",
            "concavity_worst               0.469500\n",
            "concave points_worst          0.209500\n",
            "symmetry_worst                0.361300\n",
            "fractal_dimension_worst       0.095640\n",
            "Name: 24, dtype: float64\n",
            "                      score\n",
            "area_worst               55\n",
            "texture_worst            54\n",
            "area_se                  54\n",
            "texture_mean             42\n",
            "concave points_worst     38\n",
            "concavity_worst          36\n",
            "concave points_mean      24\n",
            "smoothness_worst         20\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.9122807017543859\n",
            "[[33  2]\n",
            " [ 3 19]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.94      0.93        35\n",
            "           1       0.90      0.86      0.88        22\n",
            "\n",
            "    accuracy                           0.91        57\n",
            "   macro avg       0.91      0.90      0.91        57\n",
            "weighted avg       0.91      0.91      0.91        57\n",
            "\n",
            "0.99901104\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  15.300000\n",
            "texture_mean                 25.270000\n",
            "perimeter_mean              102.400000\n",
            "area_mean                   732.400000\n",
            "smoothness_mean               0.108200\n",
            "compactness_mean              0.169700\n",
            "concavity_mean                0.168300\n",
            "concave points_mean           0.087510\n",
            "symmetry_mean                 0.192600\n",
            "fractal_dimension_mean        0.065400\n",
            "radius_se                     0.439000\n",
            "texture_se                    1.012000\n",
            "perimeter_se                  3.498000\n",
            "area_se                      43.500000\n",
            "smoothness_se                 0.005233\n",
            "compactness_se                0.030570\n",
            "concavity_se                  0.035760\n",
            "concave points_se             0.010830\n",
            "symmetry_se                   0.017680\n",
            "fractal_dimension_se          0.002967\n",
            "radius_worst                 20.270000\n",
            "texture_worst                36.710000\n",
            "perimeter_worst             149.300000\n",
            "area_worst                 1269.000000\n",
            "smoothness_worst              0.164100\n",
            "compactness_worst             0.611000\n",
            "concavity_worst               0.633500\n",
            "concave points_worst          0.202400\n",
            "symmetry_worst                0.402700\n",
            "fractal_dimension_worst       0.098760\n",
            "Name: 28, dtype: float64\n",
            "0.9990295\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  18.630000\n",
            "texture_mean                 25.110000\n",
            "perimeter_mean              124.800000\n",
            "area_mean                  1088.000000\n",
            "smoothness_mean               0.106400\n",
            "compactness_mean              0.188700\n",
            "concavity_mean                0.231900\n",
            "concave points_mean           0.124400\n",
            "symmetry_mean                 0.218300\n",
            "fractal_dimension_mean        0.061970\n",
            "radius_se                     0.830700\n",
            "texture_se                    1.466000\n",
            "perimeter_se                  5.574000\n",
            "area_se                     105.000000\n",
            "smoothness_se                 0.006248\n",
            "compactness_se                0.033740\n",
            "concavity_se                  0.051960\n",
            "concave points_se             0.011580\n",
            "symmetry_se                   0.020070\n",
            "fractal_dimension_se          0.004560\n",
            "radius_worst                 23.150000\n",
            "texture_worst                34.010000\n",
            "perimeter_worst             160.500000\n",
            "area_worst                 1670.000000\n",
            "smoothness_worst              0.149100\n",
            "compactness_worst             0.425700\n",
            "concavity_worst               0.613300\n",
            "concave points_worst          0.184800\n",
            "symmetry_worst                0.344400\n",
            "fractal_dimension_worst       0.097820\n",
            "Name: 30, dtype: float64\n",
            "0.9990822\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  19.270000\n",
            "texture_mean                 26.470000\n",
            "perimeter_mean              127.900000\n",
            "area_mean                  1162.000000\n",
            "smoothness_mean               0.094010\n",
            "compactness_mean              0.171900\n",
            "concavity_mean                0.165700\n",
            "concave points_mean           0.075930\n",
            "symmetry_mean                 0.185300\n",
            "fractal_dimension_mean        0.062610\n",
            "radius_se                     0.555800\n",
            "texture_se                    0.606200\n",
            "perimeter_se                  3.528000\n",
            "area_se                      68.170000\n",
            "smoothness_se                 0.005015\n",
            "compactness_se                0.033180\n",
            "concavity_se                  0.034970\n",
            "concave points_se             0.009643\n",
            "symmetry_se                   0.015430\n",
            "fractal_dimension_se          0.003896\n",
            "radius_worst                 24.150000\n",
            "texture_worst                30.900000\n",
            "perimeter_worst             161.400000\n",
            "area_worst                 1813.000000\n",
            "smoothness_worst              0.150900\n",
            "compactness_worst             0.659000\n",
            "concavity_worst               0.609100\n",
            "concave points_worst          0.178500\n",
            "symmetry_worst                0.367200\n",
            "fractal_dimension_worst       0.112300\n",
            "Name: 33, dtype: float64\n",
            "0.9990422\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  16.740000\n",
            "texture_mean                 21.590000\n",
            "perimeter_mean              110.100000\n",
            "area_mean                   869.500000\n",
            "smoothness_mean               0.096100\n",
            "compactness_mean              0.133600\n",
            "concavity_mean                0.134800\n",
            "concave points_mean           0.060180\n",
            "symmetry_mean                 0.189600\n",
            "fractal_dimension_mean        0.056560\n",
            "radius_se                     0.461500\n",
            "texture_se                    0.919700\n",
            "perimeter_se                  3.008000\n",
            "area_se                      45.190000\n",
            "smoothness_se                 0.005776\n",
            "compactness_se                0.024990\n",
            "concavity_se                  0.036950\n",
            "concave points_se             0.011950\n",
            "symmetry_se                   0.027890\n",
            "fractal_dimension_se          0.002665\n",
            "radius_worst                 20.010000\n",
            "texture_worst                29.020000\n",
            "perimeter_worst             133.500000\n",
            "area_worst                 1229.000000\n",
            "smoothness_worst              0.156300\n",
            "compactness_worst             0.383500\n",
            "concavity_worst               0.540900\n",
            "concave points_worst          0.181300\n",
            "symmetry_worst                0.486300\n",
            "fractal_dimension_worst       0.086330\n",
            "Name: 35, dtype: float64\n",
            "                      score\n",
            "texture_worst            64\n",
            "area_se                  63\n",
            "concave points_worst     44\n",
            "area_worst               40\n",
            "smoothness_worst         27\n",
            "concavity_worst          25\n",
            "concave points_mean      24\n",
            "texture_mean             22\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "0.9473684210526315\n",
            "[[35  1]\n",
            " [ 2 19]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.97      0.96        36\n",
            "           1       0.95      0.90      0.93        21\n",
            "\n",
            "    accuracy                           0.95        57\n",
            "   macro avg       0.95      0.94      0.94        57\n",
            "weighted avg       0.95      0.95      0.95        57\n",
            "\n",
            "0.99916923\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  19.210000\n",
            "texture_mean                 18.570000\n",
            "perimeter_mean              125.500000\n",
            "area_mean                  1152.000000\n",
            "smoothness_mean               0.105300\n",
            "compactness_mean              0.126700\n",
            "concavity_mean                0.132300\n",
            "concave points_mean           0.089940\n",
            "symmetry_mean                 0.191700\n",
            "fractal_dimension_mean        0.059610\n",
            "radius_se                     0.727500\n",
            "texture_se                    1.193000\n",
            "perimeter_se                  4.837000\n",
            "area_se                     102.500000\n",
            "smoothness_se                 0.006458\n",
            "compactness_se                0.023060\n",
            "concavity_se                  0.029450\n",
            "concave points_se             0.015380\n",
            "symmetry_se                   0.018520\n",
            "fractal_dimension_se          0.002608\n",
            "radius_worst                 26.140000\n",
            "texture_worst                28.140000\n",
            "perimeter_worst             170.100000\n",
            "area_worst                 2145.000000\n",
            "smoothness_worst              0.162400\n",
            "compactness_worst             0.351100\n",
            "concavity_worst               0.387900\n",
            "concave points_worst          0.209100\n",
            "symmetry_worst                0.353700\n",
            "fractal_dimension_worst       0.082940\n",
            "Name: 56, dtype: float64\n",
            "0.9990878\n",
            "실제 lable : \n",
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            "예측 : \n",
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            "radius_mean                 14.250000\n",
            "texture_mean                22.150000\n",
            "perimeter_mean              96.420000\n",
            "area_mean                  645.700000\n",
            "smoothness_mean              0.104900\n",
            "compactness_mean             0.200800\n",
            "concavity_mean               0.213500\n",
            "concave points_mean          0.086530\n",
            "symmetry_mean                0.194900\n",
            "fractal_dimension_mean       0.072920\n",
            "radius_se                    0.703600\n",
            "texture_se                   1.268000\n",
            "perimeter_se                 5.373000\n",
            "area_se                     60.780000\n",
            "smoothness_se                0.009407\n",
            "compactness_se               0.070560\n",
            "concavity_se                 0.068990\n",
            "concave points_se            0.018480\n",
            "symmetry_se                  0.017000\n",
            "fractal_dimension_se         0.006113\n",
            "radius_worst                17.670000\n",
            "texture_worst               29.510000\n",
            "perimeter_worst            119.100000\n",
            "area_worst                 959.500000\n",
            "smoothness_worst             0.164000\n",
            "compactness_worst            0.624700\n",
            "concavity_worst              0.692200\n",
            "concave points_worst         0.178500\n",
            "symmetry_worst               0.284400\n",
            "fractal_dimension_worst      0.113200\n",
            "Name: 62, dtype: float64\n",
            "0.99914205\n",
            "실제 lable : \n",
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            "예측 : \n",
            "1\n",
            "radius_mean                  17.200000\n",
            "texture_mean                 24.520000\n",
            "perimeter_mean              114.200000\n",
            "area_mean                   929.400000\n",
            "smoothness_mean               0.107100\n",
            "compactness_mean              0.183000\n",
            "concavity_mean                0.169200\n",
            "concave points_mean           0.079440\n",
            "symmetry_mean                 0.192700\n",
            "fractal_dimension_mean        0.064870\n",
            "radius_se                     0.590700\n",
            "texture_se                    1.041000\n",
            "perimeter_se                  3.705000\n",
            "area_se                      69.470000\n",
            "smoothness_se                 0.005820\n",
            "compactness_se                0.056160\n",
            "concavity_se                  0.042520\n",
            "concave points_se             0.011270\n",
            "symmetry_se                   0.015270\n",
            "fractal_dimension_se          0.006299\n",
            "radius_worst                 23.320000\n",
            "texture_worst                33.820000\n",
            "perimeter_worst             151.600000\n",
            "area_worst                 1681.000000\n",
            "smoothness_worst              0.158500\n",
            "compactness_worst             0.739400\n",
            "concavity_worst               0.656600\n",
            "concave points_worst          0.189900\n",
            "symmetry_worst                0.331300\n",
            "fractal_dimension_worst       0.133900\n",
            "Name: 72, dtype: float64\n",
            "0.9990409\n",
            "실제 lable : \n",
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            "예측 : \n",
            "1\n",
            "radius_mean                  20.180000\n",
            "texture_mean                 23.970000\n",
            "perimeter_mean              143.700000\n",
            "area_mean                  1245.000000\n",
            "smoothness_mean               0.128600\n",
            "compactness_mean              0.345400\n",
            "concavity_mean                0.375400\n",
            "concave points_mean           0.160400\n",
            "symmetry_mean                 0.290600\n",
            "fractal_dimension_mean        0.081420\n",
            "radius_se                     0.931700\n",
            "texture_se                    1.885000\n",
            "perimeter_se                  8.649000\n",
            "area_se                     116.400000\n",
            "smoothness_se                 0.010380\n",
            "compactness_se                0.068350\n",
            "concavity_se                  0.109100\n",
            "concave points_se             0.025930\n",
            "symmetry_se                   0.078950\n",
            "fractal_dimension_se          0.005987\n",
            "radius_worst                 23.370000\n",
            "texture_worst                31.720000\n",
            "perimeter_worst             170.300000\n",
            "area_worst                 1623.000000\n",
            "smoothness_worst              0.163900\n",
            "compactness_worst             0.616400\n",
            "concavity_worst               0.768100\n",
            "concave points_worst          0.250800\n",
            "symmetry_worst                0.544000\n",
            "fractal_dimension_worst       0.099640\n",
            "Name: 78, dtype: float64\n",
            "0.99928194\n",
            "실제 lable : \n",
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            "예측 : \n",
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            "radius_mean                  18.460000\n",
            "texture_mean                 18.520000\n",
            "perimeter_mean              121.100000\n",
            "area_mean                  1075.000000\n",
            "smoothness_mean               0.098740\n",
            "compactness_mean              0.105300\n",
            "concavity_mean                0.133500\n",
            "concave points_mean           0.087950\n",
            "symmetry_mean                 0.213200\n",
            "fractal_dimension_mean        0.060220\n",
            "radius_se                     0.699700\n",
            "texture_se                    1.475000\n",
            "perimeter_se                  4.782000\n",
            "area_se                      80.600000\n",
            "smoothness_se                 0.006471\n",
            "compactness_se                0.016490\n",
            "concavity_se                  0.028060\n",
            "concave points_se             0.014200\n",
            "symmetry_se                   0.023700\n",
            "fractal_dimension_se          0.003755\n",
            "radius_worst                 22.930000\n",
            "texture_worst                27.680000\n",
            "perimeter_worst             152.200000\n",
            "area_worst                 1603.000000\n",
            "smoothness_worst              0.139800\n",
            "compactness_worst             0.208900\n",
            "concavity_worst               0.315700\n",
            "concave points_worst          0.164200\n",
            "symmetry_worst                0.369500\n",
            "fractal_dimension_worst       0.085790\n",
            "Name: 85, dtype: float64\n",
            "0.99905044\n",
            "실제 lable : \n",
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            "예측 : \n",
            "1\n",
            "radius_mean                  15.06000\n",
            "texture_mean                 19.83000\n",
            "perimeter_mean              100.30000\n",
            "area_mean                   705.60000\n",
            "smoothness_mean               0.10390\n",
            "compactness_mean              0.15530\n",
            "concavity_mean                0.17000\n",
            "concave points_mean           0.08815\n",
            "symmetry_mean                 0.18550\n",
            "fractal_dimension_mean        0.06284\n",
            "radius_se                     0.47680\n",
            "texture_se                    0.96440\n",
            "perimeter_se                  3.70600\n",
            "area_se                      47.14000\n",
            "smoothness_se                 0.00925\n",
            "compactness_se                0.03715\n",
            "concavity_se                  0.04867\n",
            "concave points_se             0.01851\n",
            "symmetry_se                   0.01498\n",
            "fractal_dimension_se          0.00352\n",
            "radius_worst                 18.23000\n",
            "texture_worst                24.23000\n",
            "perimeter_worst             123.50000\n",
            "area_worst                 1025.00000\n",
            "smoothness_worst              0.15510\n",
            "compactness_worst             0.42030\n",
            "concavity_worst               0.52030\n",
            "concave points_worst          0.21150\n",
            "symmetry_worst                0.28340\n",
            "fractal_dimension_worst       0.08234\n",
            "Name: 94, dtype: float64\n",
            "                      score\n",
            "texture_worst            59\n",
            "area_se                  56\n",
            "concave points_worst     49\n",
            "area_worst               41\n",
            "concave points_mean      29\n",
            "concavity_worst          28\n",
            "texture_mean             28\n",
            "radius_worst             20\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "0.9473684210526315\n",
            "[[36  0]\n",
            " [ 3 18]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      1.00      0.96        36\n",
            "           1       1.00      0.86      0.92        21\n",
            "\n",
            "    accuracy                           0.95        57\n",
            "   macro avg       0.96      0.93      0.94        57\n",
            "weighted avg       0.95      0.95      0.95        57\n",
            "\n",
            "0.99911314\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  18.450000\n",
            "texture_mean                 21.910000\n",
            "perimeter_mean              120.200000\n",
            "area_mean                  1075.000000\n",
            "smoothness_mean               0.094300\n",
            "compactness_mean              0.097090\n",
            "concavity_mean                0.115300\n",
            "concave points_mean           0.068470\n",
            "symmetry_mean                 0.169200\n",
            "fractal_dimension_mean        0.057270\n",
            "radius_se                     0.595900\n",
            "texture_se                    1.202000\n",
            "perimeter_se                  3.766000\n",
            "area_se                      68.350000\n",
            "smoothness_se                 0.006001\n",
            "compactness_se                0.014220\n",
            "concavity_se                  0.028550\n",
            "concave points_se             0.009148\n",
            "symmetry_se                   0.014920\n",
            "fractal_dimension_se          0.002205\n",
            "radius_worst                 22.520000\n",
            "texture_worst                31.390000\n",
            "perimeter_worst             145.600000\n",
            "area_worst                 1590.000000\n",
            "smoothness_worst              0.146500\n",
            "compactness_worst             0.227500\n",
            "concavity_worst               0.396500\n",
            "concave points_worst          0.137900\n",
            "symmetry_worst                0.310900\n",
            "fractal_dimension_worst       0.076100\n",
            "Name: 134, dtype: float64\n",
            "                      score\n",
            "texture_worst            55\n",
            "area_worst               54\n",
            "area_se                  50\n",
            "concave points_worst     43\n",
            "texture_mean             41\n",
            "smoothness_worst         26\n",
            "concave points_mean      20\n",
            "concavity_worst          20\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "0.9824561403508771\n",
            "[[36  0]\n",
            " [ 1 20]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.99        36\n",
            "           1       1.00      0.95      0.98        21\n",
            "\n",
            "    accuracy                           0.98        57\n",
            "   macro avg       0.99      0.98      0.98        57\n",
            "weighted avg       0.98      0.98      0.98        57\n",
            "\n",
            "0.99928397\n",
            "실제 lable : \n",
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            "예측 : \n",
            "1\n",
            "radius_mean                  27.220000\n",
            "texture_mean                 21.870000\n",
            "perimeter_mean              182.100000\n",
            "area_mean                  2250.000000\n",
            "smoothness_mean               0.109400\n",
            "compactness_mean              0.191400\n",
            "concavity_mean                0.287100\n",
            "concave points_mean           0.187800\n",
            "symmetry_mean                 0.180000\n",
            "fractal_dimension_mean        0.057700\n",
            "radius_se                     0.836100\n",
            "texture_se                    1.481000\n",
            "perimeter_se                  5.820000\n",
            "area_se                     128.700000\n",
            "smoothness_se                 0.004631\n",
            "compactness_se                0.025370\n",
            "concavity_se                  0.031090\n",
            "concave points_se             0.012410\n",
            "symmetry_se                   0.015750\n",
            "fractal_dimension_se          0.002747\n",
            "radius_worst                 33.120000\n",
            "texture_worst                32.850000\n",
            "perimeter_worst             220.800000\n",
            "area_worst                 3216.000000\n",
            "smoothness_worst              0.147200\n",
            "compactness_worst             0.403400\n",
            "concavity_worst               0.534000\n",
            "concave points_worst          0.268800\n",
            "symmetry_worst                0.285600\n",
            "fractal_dimension_worst       0.080820\n",
            "Name: 180, dtype: float64\n",
            "                      score\n",
            "texture_worst            64\n",
            "area_worst               55\n",
            "area_se                  52\n",
            "texture_mean             38\n",
            "concave points_worst     35\n",
            "concavity_worst          31\n",
            "concave points_mean      31\n",
            "smoothness_worst         25\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1.0\n",
            "[[36  0]\n",
            " [ 0 21]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        36\n",
            "           1       1.00      1.00      1.00        21\n",
            "\n",
            "    accuracy                           1.00        57\n",
            "   macro avg       1.00      1.00      1.00        57\n",
            "weighted avg       1.00      1.00      1.00        57\n",
            "\n",
            "0.99915636\n",
            "실제 lable : \n",
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            "예측 : \n",
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            "radius_mean                  23.210000\n",
            "texture_mean                 26.970000\n",
            "perimeter_mean              153.500000\n",
            "area_mean                  1670.000000\n",
            "smoothness_mean               0.095090\n",
            "compactness_mean              0.168200\n",
            "concavity_mean                0.195000\n",
            "concave points_mean           0.123700\n",
            "symmetry_mean                 0.190900\n",
            "fractal_dimension_mean        0.063090\n",
            "radius_se                     1.058000\n",
            "texture_se                    0.963500\n",
            "perimeter_se                  7.247000\n",
            "area_se                     155.800000\n",
            "smoothness_se                 0.006428\n",
            "compactness_se                0.028630\n",
            "concavity_se                  0.044970\n",
            "concave points_se             0.017160\n",
            "symmetry_se                   0.015900\n",
            "fractal_dimension_se          0.003053\n",
            "radius_worst                 31.010000\n",
            "texture_worst                34.510000\n",
            "perimeter_worst             206.000000\n",
            "area_worst                 2944.000000\n",
            "smoothness_worst              0.148100\n",
            "compactness_worst             0.412600\n",
            "concavity_worst               0.582000\n",
            "concave points_worst          0.259300\n",
            "symmetry_worst                0.310300\n",
            "fractal_dimension_worst       0.086770\n",
            "Name: 236, dtype: float64\n",
            "0.9990615\n",
            "실제 lable : \n",
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            "예측 : \n",
            "1\n",
            "radius_mean                  17.460000\n",
            "texture_mean                 39.280000\n",
            "perimeter_mean              113.400000\n",
            "area_mean                   920.600000\n",
            "smoothness_mean               0.098120\n",
            "compactness_mean              0.129800\n",
            "concavity_mean                0.141700\n",
            "concave points_mean           0.088110\n",
            "symmetry_mean                 0.180900\n",
            "fractal_dimension_mean        0.059660\n",
            "radius_se                     0.536600\n",
            "texture_se                    0.856100\n",
            "perimeter_se                  3.002000\n",
            "area_se                      49.000000\n",
            "smoothness_se                 0.004860\n",
            "compactness_se                0.027850\n",
            "concavity_se                  0.026020\n",
            "concave points_se             0.013740\n",
            "symmetry_se                   0.012260\n",
            "fractal_dimension_se          0.002759\n",
            "radius_worst                 22.510000\n",
            "texture_worst                44.870000\n",
            "perimeter_worst             141.200000\n",
            "area_worst                 1408.000000\n",
            "smoothness_worst              0.136500\n",
            "compactness_worst             0.373500\n",
            "concavity_worst               0.324100\n",
            "concave points_worst          0.206600\n",
            "symmetry_worst                0.285300\n",
            "fractal_dimension_worst       0.084960\n",
            "Name: 239, dtype: float64\n",
            "                      score\n",
            "texture_worst            64\n",
            "area_worst               60\n",
            "area_se                  53\n",
            "concave points_worst     37\n",
            "texture_mean             33\n",
            "concave points_mean      33\n",
            "concavity_worst          28\n",
            "smoothness_mean          20\n"
          ],
          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "0.9473684210526315\n",
            "[[35  1]\n",
            " [ 2 19]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.97      0.96        36\n",
            "           1       0.95      0.90      0.93        21\n",
            "\n",
            "    accuracy                           0.95        57\n",
            "   macro avg       0.95      0.94      0.94        57\n",
            "weighted avg       0.95      0.95      0.95        57\n",
            "\n",
            "0.99940467\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  20.310000\n",
            "texture_mean                 27.060000\n",
            "perimeter_mean              132.900000\n",
            "area_mean                  1288.000000\n",
            "smoothness_mean               0.100000\n",
            "compactness_mean              0.108800\n",
            "concavity_mean                0.151900\n",
            "concave points_mean           0.093330\n",
            "symmetry_mean                 0.181400\n",
            "fractal_dimension_mean        0.055720\n",
            "radius_se                     0.397700\n",
            "texture_se                    1.033000\n",
            "perimeter_se                  2.587000\n",
            "area_se                      52.340000\n",
            "smoothness_se                 0.005043\n",
            "compactness_se                0.015780\n",
            "concavity_se                  0.021170\n",
            "concave points_se             0.008185\n",
            "symmetry_se                   0.012820\n",
            "fractal_dimension_se          0.001892\n",
            "radius_worst                 24.330000\n",
            "texture_worst                39.160000\n",
            "perimeter_worst             162.300000\n",
            "area_worst                 1844.000000\n",
            "smoothness_worst              0.152200\n",
            "compactness_worst             0.294500\n",
            "concavity_worst               0.378800\n",
            "concave points_worst          0.169700\n",
            "symmetry_worst                0.315100\n",
            "fractal_dimension_worst       0.079990\n",
            "Name: 260, dtype: float64\n",
            "0.9993511\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  17.190000\n",
            "texture_mean                 22.070000\n",
            "perimeter_mean              111.600000\n",
            "area_mean                   928.300000\n",
            "smoothness_mean               0.097260\n",
            "compactness_mean              0.089950\n",
            "concavity_mean                0.090610\n",
            "concave points_mean           0.065270\n",
            "symmetry_mean                 0.186700\n",
            "fractal_dimension_mean        0.055800\n",
            "radius_se                     0.420300\n",
            "texture_se                    0.738300\n",
            "perimeter_se                  2.819000\n",
            "area_se                      45.420000\n",
            "smoothness_se                 0.004493\n",
            "compactness_se                0.012060\n",
            "concavity_se                  0.020480\n",
            "concave points_se             0.009875\n",
            "symmetry_se                   0.011440\n",
            "fractal_dimension_se          0.001575\n",
            "radius_worst                 21.580000\n",
            "texture_worst                29.330000\n",
            "perimeter_worst             140.500000\n",
            "area_worst                 1436.000000\n",
            "smoothness_worst              0.155800\n",
            "compactness_worst             0.256700\n",
            "concavity_worst               0.388900\n",
            "concave points_worst          0.198400\n",
            "symmetry_worst                0.321600\n",
            "fractal_dimension_worst       0.075700\n",
            "Name: 264, dtype: float64\n",
            "0.99942565\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  20.730000\n",
            "texture_mean                 31.120000\n",
            "perimeter_mean              135.700000\n",
            "area_mean                  1419.000000\n",
            "smoothness_mean               0.094690\n",
            "compactness_mean              0.114300\n",
            "concavity_mean                0.136700\n",
            "concave points_mean           0.086460\n",
            "symmetry_mean                 0.176900\n",
            "fractal_dimension_mean        0.056740\n",
            "radius_se                     1.172000\n",
            "texture_se                    1.617000\n",
            "perimeter_se                  7.749000\n",
            "area_se                     199.700000\n",
            "smoothness_se                 0.004551\n",
            "compactness_se                0.014780\n",
            "concavity_se                  0.021430\n",
            "concave points_se             0.009280\n",
            "symmetry_se                   0.013670\n",
            "fractal_dimension_se          0.002299\n",
            "radius_worst                 32.490000\n",
            "texture_worst                47.160000\n",
            "perimeter_worst             214.000000\n",
            "area_worst                 3432.000000\n",
            "smoothness_worst              0.140100\n",
            "compactness_worst             0.264400\n",
            "concavity_worst               0.344200\n",
            "concave points_worst          0.165900\n",
            "symmetry_worst                0.286800\n",
            "fractal_dimension_worst       0.082180\n",
            "Name: 265, dtype: float64\n",
            "                      score\n",
            "texture_worst            54\n",
            "area_worst               54\n",
            "texture_mean             51\n",
            "area_se                  39\n",
            "concave points_worst     39\n",
            "concavity_worst          30\n",
            "radius_worst             29\n",
            "smoothness_worst         23\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.9824561403508771\n",
            "[[36  0]\n",
            " [ 1 20]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.99        36\n",
            "           1       1.00      0.95      0.98        21\n",
            "\n",
            "    accuracy                           0.98        57\n",
            "   macro avg       0.99      0.98      0.98        57\n",
            "weighted avg       0.98      0.98      0.98        57\n",
            "\n",
            "0.9990633\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  20.340000\n",
            "texture_mean                 21.510000\n",
            "perimeter_mean              135.900000\n",
            "area_mean                  1264.000000\n",
            "smoothness_mean               0.117000\n",
            "compactness_mean              0.187500\n",
            "concavity_mean                0.256500\n",
            "concave points_mean           0.150400\n",
            "symmetry_mean                 0.256900\n",
            "fractal_dimension_mean        0.066700\n",
            "radius_se                     0.570200\n",
            "texture_se                    1.023000\n",
            "perimeter_se                  4.012000\n",
            "area_se                      69.060000\n",
            "smoothness_se                 0.005485\n",
            "compactness_se                0.024310\n",
            "concavity_se                  0.031900\n",
            "concave points_se             0.013690\n",
            "symmetry_se                   0.027680\n",
            "fractal_dimension_se          0.003345\n",
            "radius_worst                 25.300000\n",
            "texture_worst                31.860000\n",
            "perimeter_worst             171.100000\n",
            "area_worst                 1938.000000\n",
            "smoothness_worst              0.159200\n",
            "compactness_worst             0.449200\n",
            "concavity_worst               0.534400\n",
            "concave points_worst          0.268500\n",
            "symmetry_worst                0.555800\n",
            "fractal_dimension_worst       0.102400\n",
            "Name: 323, dtype: float64\n",
            "0.9991295\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  16.270000\n",
            "texture_mean                 20.710000\n",
            "perimeter_mean              106.900000\n",
            "area_mean                   813.700000\n",
            "smoothness_mean               0.116900\n",
            "compactness_mean              0.131900\n",
            "concavity_mean                0.147800\n",
            "concave points_mean           0.084880\n",
            "symmetry_mean                 0.194800\n",
            "fractal_dimension_mean        0.062770\n",
            "radius_se                     0.437500\n",
            "texture_se                    1.232000\n",
            "perimeter_se                  3.270000\n",
            "area_se                      44.410000\n",
            "smoothness_se                 0.006697\n",
            "compactness_se                0.020830\n",
            "concavity_se                  0.032480\n",
            "concave points_se             0.013920\n",
            "symmetry_se                   0.015360\n",
            "fractal_dimension_se          0.002789\n",
            "radius_worst                 19.280000\n",
            "texture_worst                30.380000\n",
            "perimeter_worst             129.800000\n",
            "area_worst                 1121.000000\n",
            "smoothness_worst              0.159000\n",
            "compactness_worst             0.294700\n",
            "concavity_worst               0.359700\n",
            "concave points_worst          0.158300\n",
            "symmetry_worst                0.310300\n",
            "fractal_dimension_worst       0.082000\n",
            "Name: 328, dtype: float64\n",
            "0.9992157\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  17.060000\n",
            "texture_mean                 21.000000\n",
            "perimeter_mean              111.800000\n",
            "area_mean                   918.600000\n",
            "smoothness_mean               0.111900\n",
            "compactness_mean              0.105600\n",
            "concavity_mean                0.150800\n",
            "concave points_mean           0.099340\n",
            "symmetry_mean                 0.172700\n",
            "fractal_dimension_mean        0.060710\n",
            "radius_se                     0.816100\n",
            "texture_se                    2.129000\n",
            "perimeter_se                  6.076000\n",
            "area_se                      87.170000\n",
            "smoothness_se                 0.006455\n",
            "compactness_se                0.017970\n",
            "concavity_se                  0.045020\n",
            "concave points_se             0.017440\n",
            "symmetry_se                   0.018290\n",
            "fractal_dimension_se          0.003733\n",
            "radius_worst                 20.990000\n",
            "texture_worst                33.150000\n",
            "perimeter_worst             143.200000\n",
            "area_worst                 1362.000000\n",
            "smoothness_worst              0.144900\n",
            "compactness_worst             0.205300\n",
            "concavity_worst               0.392000\n",
            "concave points_worst          0.182700\n",
            "symmetry_worst                0.262300\n",
            "fractal_dimension_worst       0.075990\n",
            "Name: 335, dtype: float64\n",
            "0.999037\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  18.770000\n",
            "texture_mean                 21.430000\n",
            "perimeter_mean              122.900000\n",
            "area_mean                  1092.000000\n",
            "smoothness_mean               0.091160\n",
            "compactness_mean              0.140200\n",
            "concavity_mean                0.106000\n",
            "concave points_mean           0.060900\n",
            "symmetry_mean                 0.195300\n",
            "fractal_dimension_mean        0.060830\n",
            "radius_se                     0.642200\n",
            "texture_se                    1.530000\n",
            "perimeter_se                  4.369000\n",
            "area_se                      88.250000\n",
            "smoothness_se                 0.007548\n",
            "compactness_se                0.038970\n",
            "concavity_se                  0.039140\n",
            "concave points_se             0.018160\n",
            "symmetry_se                   0.021680\n",
            "fractal_dimension_se          0.004445\n",
            "radius_worst                 24.540000\n",
            "texture_worst                34.370000\n",
            "perimeter_worst             161.100000\n",
            "area_worst                 1873.000000\n",
            "smoothness_worst              0.149800\n",
            "compactness_worst             0.482700\n",
            "concavity_worst               0.463400\n",
            "concave points_worst          0.204800\n",
            "symmetry_worst                0.367900\n",
            "fractal_dimension_worst       0.098700\n",
            "Name: 337, dtype: float64\n",
            "0.99922943\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  23.510000\n",
            "texture_mean                 24.270000\n",
            "perimeter_mean              155.100000\n",
            "area_mean                  1747.000000\n",
            "smoothness_mean               0.106900\n",
            "compactness_mean              0.128300\n",
            "concavity_mean                0.230800\n",
            "concave points_mean           0.141000\n",
            "symmetry_mean                 0.179700\n",
            "fractal_dimension_mean        0.055060\n",
            "radius_se                     1.009000\n",
            "texture_se                    0.924500\n",
            "perimeter_se                  6.462000\n",
            "area_se                     164.100000\n",
            "smoothness_se                 0.006292\n",
            "compactness_se                0.019710\n",
            "concavity_se                  0.035820\n",
            "concave points_se             0.013010\n",
            "symmetry_se                   0.014790\n",
            "fractal_dimension_se          0.003118\n",
            "radius_worst                 30.670000\n",
            "texture_worst                30.730000\n",
            "perimeter_worst             202.400000\n",
            "area_worst                 2906.000000\n",
            "smoothness_worst              0.151500\n",
            "compactness_worst             0.267800\n",
            "concavity_worst               0.481900\n",
            "concave points_worst          0.208900\n",
            "symmetry_worst                0.259300\n",
            "fractal_dimension_worst       0.077380\n",
            "Name: 339, dtype: float64\n",
            "0.99911207\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  20.440000\n",
            "texture_mean                 21.780000\n",
            "perimeter_mean              133.800000\n",
            "area_mean                  1293.000000\n",
            "smoothness_mean               0.091500\n",
            "compactness_mean              0.113100\n",
            "concavity_mean                0.097990\n",
            "concave points_mean           0.077850\n",
            "symmetry_mean                 0.161800\n",
            "fractal_dimension_mean        0.055570\n",
            "radius_se                     0.578100\n",
            "texture_se                    0.916800\n",
            "perimeter_se                  4.218000\n",
            "area_se                      72.440000\n",
            "smoothness_se                 0.006208\n",
            "compactness_se                0.019060\n",
            "concavity_se                  0.023750\n",
            "concave points_se             0.014610\n",
            "symmetry_se                   0.014450\n",
            "fractal_dimension_se          0.001906\n",
            "radius_worst                 24.310000\n",
            "texture_worst                26.370000\n",
            "perimeter_worst             161.200000\n",
            "area_worst                 1780.000000\n",
            "smoothness_worst              0.132700\n",
            "compactness_worst             0.237600\n",
            "concavity_worst               0.270200\n",
            "concave points_worst          0.176500\n",
            "symmetry_worst                0.260900\n",
            "fractal_dimension_worst       0.067350\n",
            "Name: 365, dtype: float64\n",
            "                      score\n",
            "texture_worst            60\n",
            "area_se                  57\n",
            "area_worst               49\n",
            "texture_mean             38\n",
            "smoothness_worst         34\n",
            "concave points_worst     33\n",
            "concavity_worst          32\n",
            "concave points_mean      28\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.9473684210526315\n",
            "[[34  2]\n",
            " [ 1 20]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.94      0.96        36\n",
            "           1       0.91      0.95      0.93        21\n",
            "\n",
            "    accuracy                           0.95        57\n",
            "   macro avg       0.94      0.95      0.94        57\n",
            "weighted avg       0.95      0.95      0.95        57\n",
            "\n",
            "0.99909806\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  15.490000\n",
            "texture_mean                 19.970000\n",
            "perimeter_mean              102.400000\n",
            "area_mean                   744.700000\n",
            "smoothness_mean               0.116000\n",
            "compactness_mean              0.156200\n",
            "concavity_mean                0.189100\n",
            "concave points_mean           0.091130\n",
            "symmetry_mean                 0.192900\n",
            "fractal_dimension_mean        0.067440\n",
            "radius_se                     0.647000\n",
            "texture_se                    1.331000\n",
            "perimeter_se                  4.675000\n",
            "area_se                      66.910000\n",
            "smoothness_se                 0.007269\n",
            "compactness_se                0.029280\n",
            "concavity_se                  0.049720\n",
            "concave points_se             0.016390\n",
            "symmetry_se                   0.018520\n",
            "fractal_dimension_se          0.004232\n",
            "radius_worst                 21.200000\n",
            "texture_worst                29.410000\n",
            "perimeter_worst             142.100000\n",
            "area_worst                 1359.000000\n",
            "smoothness_worst              0.168100\n",
            "compactness_worst             0.391300\n",
            "concavity_worst               0.555300\n",
            "concave points_worst          0.212100\n",
            "symmetry_worst                0.318700\n",
            "fractal_dimension_worst       0.101900\n",
            "Name: 392, dtype: float64\n",
            "0.9991026\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  21.610000\n",
            "texture_mean                 22.280000\n",
            "perimeter_mean              144.400000\n",
            "area_mean                  1407.000000\n",
            "smoothness_mean               0.116700\n",
            "compactness_mean              0.208700\n",
            "concavity_mean                0.281000\n",
            "concave points_mean           0.156200\n",
            "symmetry_mean                 0.216200\n",
            "fractal_dimension_mean        0.066060\n",
            "radius_se                     0.624200\n",
            "texture_se                    0.920900\n",
            "perimeter_se                  4.158000\n",
            "area_se                      80.990000\n",
            "smoothness_se                 0.005215\n",
            "compactness_se                0.037260\n",
            "concavity_se                  0.047180\n",
            "concave points_se             0.012880\n",
            "symmetry_se                   0.020450\n",
            "fractal_dimension_se          0.004028\n",
            "radius_worst                 26.230000\n",
            "texture_worst                28.740000\n",
            "perimeter_worst             172.000000\n",
            "area_worst                 2081.000000\n",
            "smoothness_worst              0.150200\n",
            "compactness_worst             0.571700\n",
            "concavity_worst               0.705300\n",
            "concave points_worst          0.242200\n",
            "symmetry_worst                0.382800\n",
            "fractal_dimension_worst       0.100700\n",
            "Name: 393, dtype: float64\n",
            "0.99932396\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  17.990000\n",
            "texture_mean                 20.660000\n",
            "perimeter_mean              117.800000\n",
            "area_mean                   991.700000\n",
            "smoothness_mean               0.103600\n",
            "compactness_mean              0.130400\n",
            "concavity_mean                0.120100\n",
            "concave points_mean           0.088240\n",
            "symmetry_mean                 0.199200\n",
            "fractal_dimension_mean        0.060690\n",
            "radius_se                     0.453700\n",
            "texture_se                    0.873300\n",
            "perimeter_se                  3.061000\n",
            "area_se                      49.810000\n",
            "smoothness_se                 0.007231\n",
            "compactness_se                0.027720\n",
            "concavity_se                  0.025090\n",
            "concave points_se             0.014800\n",
            "symmetry_se                   0.014140\n",
            "fractal_dimension_se          0.003336\n",
            "radius_worst                 21.080000\n",
            "texture_worst                25.410000\n",
            "perimeter_worst             138.100000\n",
            "area_worst                 1349.000000\n",
            "smoothness_worst              0.148200\n",
            "compactness_worst             0.373500\n",
            "concavity_worst               0.330100\n",
            "concave points_worst          0.197400\n",
            "symmetry_worst                0.306000\n",
            "fractal_dimension_worst       0.085030\n",
            "Name: 408, dtype: float64\n",
            "0.9991026\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  15.500000\n",
            "texture_mean                 21.080000\n",
            "perimeter_mean              102.900000\n",
            "area_mean                   803.100000\n",
            "smoothness_mean               0.112000\n",
            "compactness_mean              0.157100\n",
            "concavity_mean                0.152200\n",
            "concave points_mean           0.084810\n",
            "symmetry_mean                 0.208500\n",
            "fractal_dimension_mean        0.068640\n",
            "radius_se                     1.370000\n",
            "texture_se                    1.213000\n",
            "perimeter_se                  9.424000\n",
            "area_se                     176.500000\n",
            "smoothness_se                 0.008198\n",
            "compactness_se                0.038890\n",
            "concavity_se                  0.044930\n",
            "concave points_se             0.021390\n",
            "symmetry_se                   0.020180\n",
            "fractal_dimension_se          0.005815\n",
            "radius_worst                 23.170000\n",
            "texture_worst                27.650000\n",
            "perimeter_worst             157.100000\n",
            "area_worst                 1748.000000\n",
            "smoothness_worst              0.151700\n",
            "compactness_worst             0.400200\n",
            "concavity_worst               0.421100\n",
            "concave points_worst          0.213400\n",
            "symmetry_worst                0.300300\n",
            "fractal_dimension_worst       0.104800\n",
            "Name: 417, dtype: float64\n",
            "0.99912447\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  17.750000\n",
            "texture_mean                 28.030000\n",
            "perimeter_mean              117.300000\n",
            "area_mean                   981.600000\n",
            "smoothness_mean               0.099970\n",
            "compactness_mean              0.131400\n",
            "concavity_mean                0.169800\n",
            "concave points_mean           0.082930\n",
            "symmetry_mean                 0.171300\n",
            "fractal_dimension_mean        0.059160\n",
            "radius_se                     0.389700\n",
            "texture_se                    1.077000\n",
            "perimeter_se                  2.873000\n",
            "area_se                      43.950000\n",
            "smoothness_se                 0.004714\n",
            "compactness_se                0.020150\n",
            "concavity_se                  0.036970\n",
            "concave points_se             0.011100\n",
            "symmetry_se                   0.012370\n",
            "fractal_dimension_se          0.002556\n",
            "radius_worst                 21.530000\n",
            "texture_worst                38.540000\n",
            "perimeter_worst             145.400000\n",
            "area_worst                 1437.000000\n",
            "smoothness_worst              0.140100\n",
            "compactness_worst             0.376200\n",
            "concavity_worst               0.639900\n",
            "concave points_worst          0.197000\n",
            "symmetry_worst                0.297200\n",
            "fractal_dimension_worst       0.090750\n",
            "Name: 446, dtype: float64\n",
            "0.99918014\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  21.100000\n",
            "texture_mean                 20.520000\n",
            "perimeter_mean              138.100000\n",
            "area_mean                  1384.000000\n",
            "smoothness_mean               0.096840\n",
            "compactness_mean              0.117500\n",
            "concavity_mean                0.157200\n",
            "concave points_mean           0.115500\n",
            "symmetry_mean                 0.155400\n",
            "fractal_dimension_mean        0.056610\n",
            "radius_se                     0.664300\n",
            "texture_se                    1.361000\n",
            "perimeter_se                  4.542000\n",
            "area_se                      81.890000\n",
            "smoothness_se                 0.005467\n",
            "compactness_se                0.020750\n",
            "concavity_se                  0.031850\n",
            "concave points_se             0.014660\n",
            "symmetry_se                   0.010290\n",
            "fractal_dimension_se          0.002205\n",
            "radius_worst                 25.680000\n",
            "texture_worst                32.070000\n",
            "perimeter_worst             168.200000\n",
            "area_worst                 2022.000000\n",
            "smoothness_worst              0.136800\n",
            "compactness_worst             0.310100\n",
            "concavity_worst               0.439900\n",
            "concave points_worst          0.228000\n",
            "symmetry_worst                0.226800\n",
            "fractal_dimension_worst       0.074250\n",
            "Name: 449, dtype: float64\n",
            "0.999361\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  19.590000\n",
            "texture_mean                 25.000000\n",
            "perimeter_mean              127.700000\n",
            "area_mean                  1191.000000\n",
            "smoothness_mean               0.103200\n",
            "compactness_mean              0.098710\n",
            "concavity_mean                0.165500\n",
            "concave points_mean           0.090630\n",
            "symmetry_mean                 0.166300\n",
            "fractal_dimension_mean        0.053910\n",
            "radius_se                     0.467400\n",
            "texture_se                    1.375000\n",
            "perimeter_se                  2.916000\n",
            "area_se                      56.180000\n",
            "smoothness_se                 0.011900\n",
            "compactness_se                0.019290\n",
            "concavity_se                  0.049070\n",
            "concave points_se             0.014990\n",
            "symmetry_se                   0.016410\n",
            "fractal_dimension_se          0.001807\n",
            "radius_worst                 21.440000\n",
            "texture_worst                30.960000\n",
            "perimeter_worst             139.800000\n",
            "area_worst                 1421.000000\n",
            "smoothness_worst              0.152800\n",
            "compactness_worst             0.184500\n",
            "concavity_worst               0.397700\n",
            "concave points_worst          0.146600\n",
            "symmetry_worst                0.229300\n",
            "fractal_dimension_worst       0.060910\n",
            "Name: 451, dtype: float64\n",
            "                      score\n",
            "area_se                  56\n",
            "texture_worst            51\n",
            "area_worst               50\n",
            "concave points_worst     35\n",
            "concavity_worst          32\n",
            "concave points_mean      29\n",
            "texture_mean             27\n",
            "perimeter_worst          26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.9821428571428571\n",
            "[[35  0]\n",
            " [ 1 20]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.99        35\n",
            "           1       1.00      0.95      0.98        21\n",
            "\n",
            "    accuracy                           0.98        56\n",
            "   macro avg       0.99      0.98      0.98        56\n",
            "weighted avg       0.98      0.98      0.98        56\n",
            "\n",
            "0.9990306\n",
            "실제 lable : \n",
            "1\n",
            "예측 : \n",
            "1\n",
            "radius_mean                  20.590000\n",
            "texture_mean                 21.240000\n",
            "perimeter_mean              137.800000\n",
            "area_mean                  1320.000000\n",
            "smoothness_mean               0.108500\n",
            "compactness_mean              0.164400\n",
            "concavity_mean                0.218800\n",
            "concave points_mean           0.112100\n",
            "symmetry_mean                 0.184800\n",
            "fractal_dimension_mean        0.062220\n",
            "radius_se                     0.590400\n",
            "texture_se                    1.216000\n",
            "perimeter_se                  4.206000\n",
            "area_se                      75.090000\n",
            "smoothness_se                 0.006666\n",
            "compactness_se                0.027910\n",
            "concavity_se                  0.040620\n",
            "concave points_se             0.014790\n",
            "symmetry_se                   0.011170\n",
            "fractal_dimension_se          0.003727\n",
            "radius_worst                 23.860000\n",
            "texture_worst                30.760000\n",
            "perimeter_worst             163.200000\n",
            "area_worst                 1760.000000\n",
            "smoothness_worst              0.146400\n",
            "compactness_worst             0.359700\n",
            "concavity_worst               0.517900\n",
            "concave points_worst          0.211300\n",
            "symmetry_worst                0.248000\n",
            "fractal_dimension_worst       0.089990\n",
            "Name: 499, dtype: float64\n",
            "                      score\n",
            "texture_worst            65\n",
            "area_se                  51\n",
            "area_worst               45\n",
            "concave points_worst     44\n",
            "texture_mean             32\n",
            "concavity_worst          28\n",
            "smoothness_worst         26\n",
            "concave points_mean      25\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}