Preventing Overfitting in Decision Trees

tuanavu · Mar 27, 2016 · 2939e81 · 2939e81
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diff --git a/.../.ipynb_checkpoints/module-6-decision-tree-practical-assignment-Graphlab-checkpoint.ipynb b/.../.ipynb_checkpoints/module-6-decision-tree-practical-assignment-Graphlab-checkpoint.ipynb
@@ -1352,7 +1352,7 @@
     "    prediction = data.apply(lambda x: classify(tree, x))    \n",
     "    # Once you've made the predictions, calculate the classification error and return it\n",
     "    ## YOUR CODE HERE\n",
-    "    num_of_mistakes = (prediction != data['safe_loans']).sum()/float(len(data))\n",
+    "    num_of_mistakes = (prediction != data[target]).sum()/float(len(data))\n",
     "    return num_of_mistakes"
    ]
   },

diff --git a/...classification/assigment/week4/module-6-decision-tree-practical-assignment-Graphlab.ipynb b/...classification/assigment/week4/module-6-decision-tree-practical-assignment-Graphlab.ipynb
@@ -1352,7 +1352,7 @@
     "    prediction = data.apply(lambda x: classify(tree, x))    \n",
     "    # Once you've made the predictions, calculate the classification error and return it\n",
     "    ## YOUR CODE HERE\n",
-    "    num_of_mistakes = (prediction != data['safe_loans']).sum()/float(len(data))\n",
+    "    num_of_mistakes = (prediction != data[target]).sum()/float(len(data))\n",
     "    return num_of_mistakes"
    ]
   },