Update decision tree plot code

README.md

@@ -21,14 +21,12 @@ In order to prepare data, train, evaluate, and visualize a decision tree, we wil
 ```python
 import numpy as np 
 import pandas as pd 
+import matplotlib.pyplot as plt
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier 
 from sklearn.metrics import accuracy_score
-from sklearn.tree import export_graphviz
 from sklearn.preprocessing import OneHotEncoder
-from IPython.display import Image  
-from sklearn.tree import export_graphviz
-from pydotplus import graph_from_dot_data
+from sklearn import tree
 ```
 
 The play tennis dataset is available in the repo as `'tennis.csv'`. For this step, we'll start by importing the csv file as a pandas DataFrame.
@@ -71,39 +69,39 @@ df.head()
   </thead>
   <tbody>
     <tr>
-      <th>0</th>
+      <td>0</td>
       <td>sunny</td>
       <td>hot</td>
       <td>high</td>
       <td>False</td>
       <td>no</td>
     </tr>
     <tr>
-      <th>1</th>
+      <td>1</td>
       <td>sunny</td>
       <td>hot</td>
       <td>high</td>
       <td>True</td>
       <td>no</td>
     </tr>
     <tr>
-      <th>2</th>
+      <td>2</td>
       <td>overcast</td>
       <td>hot</td>
       <td>high</td>
       <td>False</td>
       <td>yes</td>
     </tr>
     <tr>
-      <th>3</th>
+      <td>3</td>
       <td>rainy</td>
       <td>mild</td>
       <td>high</td>
       <td>False</td>
       <td>yes</td>
     </tr>
     <tr>
-      <th>4</th>
+      <td>4</td>
       <td>rainy</td>
       <td>cool</td>
       <td>normal</td>
@@ -122,8 +120,8 @@ Before we do anything we'll want to split our data into **_training_** and **_te
 
 
 ```python
-X = df.loc[:, ['outlook', 'temp', 'humidity', 'windy']]
-y = df.loc[:, 'play']
+X = df[['outlook', 'temp', 'humidity', 'windy']]
+y = df[['play']]
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)
 ```
@@ -181,7 +179,7 @@ ohe_df.head()
   </thead>
   <tbody>
     <tr>
-      <th>0</th>
+      <td>0</td>
       <td>0.0</td>
       <td>0.0</td>
       <td>1.0</td>
@@ -194,7 +192,7 @@ ohe_df.head()
       <td>0.0</td>
     </tr>
     <tr>
-      <th>1</th>
+      <td>1</td>
       <td>1.0</td>
       <td>0.0</td>
       <td>0.0</td>
@@ -207,7 +205,7 @@ ohe_df.head()
       <td>0.0</td>
     </tr>
     <tr>
-      <th>2</th>
+      <td>2</td>
       <td>0.0</td>
       <td>0.0</td>
       <td>1.0</td>
@@ -220,7 +218,7 @@ ohe_df.head()
       <td>1.0</td>
     </tr>
     <tr>
-      <th>3</th>
+      <td>3</td>
       <td>0.0</td>
       <td>1.0</td>
       <td>0.0</td>
@@ -233,7 +231,7 @@ ohe_df.head()
       <td>1.0</td>
     </tr>
     <tr>
-      <th>4</th>
+      <td>4</td>
       <td>0.0</td>
       <td>1.0</td>
       <td>0.0</td>
@@ -268,43 +266,33 @@ clf.fit(X_train_ohe, y_train)
 
 
 
-    DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,
-                           max_features=None, max_leaf_nodes=None,
+    DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',
+                           max_depth=None, max_features=None, max_leaf_nodes=None,
                            min_impurity_decrease=0.0, min_impurity_split=None,
                            min_samples_leaf=1, min_samples_split=2,
-                           min_weight_fraction_leaf=0.0, presort=False,
+                           min_weight_fraction_leaf=0.0, presort='deprecated',
                            random_state=None, splitter='best')
 
 
 
 ## Plot the decision tree 
 
-You can see what rules the tree learned by plotting this decision tree. To do this, you need to use additional packages such as `pytdotplus`. 
-
-> **Note:** If you are run into errors while generating the plot, you probably need to install `python-graphviz` in your machine using `conda install python-graphviz`. 
+You can see what rules the tree learned by plotting this decision tree, using matplotlib and sklearn's `plot_tree` function.
 
 
 ```python
-# Create DOT data
-dot_data = export_graphviz(clf, out_file=None, 
-                           feature_names=ohe_df.columns,  
-                           class_names=np.unique(y).astype('str'), 
-                           filled=True, rounded=True, special_characters=True)
-
-# Draw graph
-graph = graph_from_dot_data(dot_data)  
-
-# Show graph
-Image(graph.create_png())
+fig, axes = plt.subplots(nrows = 1,ncols = 1, figsize = (3,3), dpi=300)
+tree.plot_tree(clf,
+               feature_names = ohe_df.columns, 
+               class_names=np.unique(y).astype('str'),
+               filled = True)
+plt.show()
 ```
 
 
-
-
 ![png](index_files/index_11_0.png)
 
 
-
 ## Evaluate the predictive performance
 
 Now that we have a trained model, we can generate some predictions, and go on to see how accurate our predictions are. We can use a simple accuracy measure, AUC, a confusion matrix, or all of them. This step is performed in the exactly the same manner, so it doesn't matter which classifier you are dealing with.