Fixed minor issue in decision tree tutorial

Author: dhavalsays

ML/9_decision_tree/9_decision_tree.ipynb

@@ -9,18 +9,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -101,7 +99,7 @@
        "4  google  computer programmer  bachelors                      0"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -113,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,21 +120,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
     "target = df['salary_more_then_100k']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.preprocessing import LabelEncoder\n",
@@ -147,18 +141,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "inputs['company_n'] = le_company.fit_transform(inputs['company'])\n",
-    "inputs['job_n'] = le_company.fit_transform(inputs['job'])\n",
-    "inputs['degree_n'] = le_company.fit_transform(inputs['degree'])"
+    "inputs['job_n'] = le_job.fit_transform(inputs['job'])\n",
+    "inputs['degree_n'] = le_degree.fit_transform(inputs['degree'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -359,7 +353,7 @@
        "15    facebook  computer programmer    masters          1      1         1"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -370,18 +364,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "inputs_n = inputs.drop(['company','job','degree'],axis='columns')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -531,7 +523,7 @@
        "15          1      1         1"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -542,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -569,7 +561,7 @@
        "Name: salary_more_then_100k, dtype: int64"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -580,10 +572,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn import tree\n",
@@ -592,20 +582,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
        "            max_features=None, max_leaf_nodes=None,\n",
-       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
-       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
-       "            presort=False, random_state=None, splitter='best')"
+       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "            min_samples_leaf=1, min_samples_split=2,\n",
+       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
+       "            splitter='best')"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -616,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -625,7 +616,7 @@
        "1.0"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -643,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
@@ -654,7 +645,7 @@
        "array([0], dtype=int64)"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 15,
    "metadata": {
     "scrolled": true
    },
@@ -683,7 +674,7 @@
        "array([1], dtype=int64)"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -709,7 +700,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,