remove mixup with logistic reg example

vertica · Oct 18, 2022 · df05174 · df05174
1 parent 274347e
commit df05174
Showing 1 changed file with 17 additions and 12 deletions.
diff --git a/...e Essentials/Module - Classification/Essentials_Classification_RandomForest_Example.ipynb b/...e Essentials/Module - Classification/Essentials_Classification_RandomForest_Example.ipynb
@@ -1166,18 +1166,7 @@
    "id": "7b2424cd-2121-46cd-b749-0e531a8a30e6",
    "metadata": {},
    "source": [
-    "Importing the library and create a random forest model object.\n",
-    "\n",
-    "```python\n",
-    "from verticapy.learn.linear_model import LogisticRegression\n",
-    "model = LogisticRegression(\"public.LR_default\")\n",
-    "```\n",
-    "\n",
-    "Fit the model to the data:\n",
-    "\n",
-    "```python\n",
-    "model.fit(data, [\"credit_balance\", \"income\",\"student\"], \"default\")\n",
-    "```\n"
+    "Importing the library and create a random forest model object."
    ]
   },
   {
@@ -1191,6 +1180,14 @@
     "model = RandomForestClassifier(\"public.RF_iris\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f6570975",
+   "metadata": {},
+   "source": [
+    "Fit the model with the data:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -1424,6 +1421,14 @@
     "model.score()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6ef331d7-dbd7-4e9c-9001-b9d5137ea74a",
+   "metadata": {},
+   "source": [
+    "This shows the efficacy of random forests towards classifying multi-class problems. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c02f25ee",