[Python] Decision Tree.ipynb

Kernel: Python 3

Decision Tree

Data preprocessing

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split # for training and testing split
from sklearn.preprocessing import StandardScaler # for Feature scaling
from sklearn.tree import DecisionTreeClassifier # for classifier
from sklearn.metrics import confusion_matrix # for making confusion matrix
from matplotlib.colors import ListedColormap # for visualisation
%matplotlib inline
plt.rcParams['figure.figsize'] = [14, 8]

# Importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

dataset.head(10)

# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)

X_train[0:10]

array([[    27,  57000],
       [    46,  28000],
       [    39, 134000],
       [    44,  39000],
       [    57,  26000],
       [    32, 120000],
       [    41,  52000],
       [    48,  74000],
       [    26,  86000],
       [    22,  81000]])

X_test[0:10]

array([[    46,  22000],
       [    59,  88000],
       [    28,  44000],
       [    48,  96000],
       [    29,  28000],
       [    30,  62000],
       [    47, 107000],
       [    29,  83000],
       [    40,  75000],
       [    42,  65000]])

y_train[0:10]

array([0, 1, 1, 0, 1, 1, 0, 1, 0, 0])

y_test[0:10]

array([0, 1, 0, 1, 0, 0, 1, 0, 0, 0])

# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

/home/baka/Programs/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.
  warnings.warn(msg, DataConversionWarning)

X_train[0:10]

array([[-1.06675246, -0.38634438],
       [ 0.79753468, -1.22993871],
       [ 0.11069205,  1.853544  ],
       [ 0.60129393, -0.90995465],
       [ 1.87685881, -1.28811763],
       [-0.57615058,  1.44629156],
       [ 0.3069328 , -0.53179168],
       [ 0.99377543,  0.10817643],
       [-1.16487283,  0.45724994],
       [-1.55735433,  0.31180264]])

X_test[0:10]

array([[ 0.79753468, -1.40447546],
       [ 2.07309956,  0.51542886],
       [-0.96863208, -0.76450736],
       [ 0.99377543,  0.74814454],
       [-0.87051171, -1.22993871],
       [-0.77239133, -0.24089709],
       [ 0.89565505,  1.06812859],
       [-0.87051171,  0.36998156],
       [ 0.20881242,  0.13726589],
       [ 0.40505317, -0.15362871]])

Fitting Decision Tree Classifier to the Training set

classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 42)
classifier.fit(X_train, y_train)

DecisionTreeClassifier(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, random_state=42,
            splitter='best')

Predicting the Test set results

y_pred = classifier.predict(X_test)

y_pred[0:10]

array([1, 1, 0, 0, 0, 0, 1, 0, 0, 0])

y_test[0:10]

array([0, 1, 0, 1, 0, 0, 1, 0, 0, 0])

Making the Confusion Matrix

cm = confusion_matrix(y_test, y_pred)
cm

array([[46,  6],
       [ 7, 21]])

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classifier made 46 + 21 = 67 correct prediction and 7 + 6 = 13 incoreect predictions.

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Visualising the Training set results

X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green'))(i), label = j, edgecolors = 'white', linewidth = 0.7)
plt.title('Decision Tree (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

Visualising the Test set results

X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green'))(i), label = j, edgecolors = 'white', linewidth = 0.7)
plt.title('Decision Tree (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

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Things to remmember while making decison tree classifier:

• Normally it overfits the data. As you can see in above training set, it tries to catch all the red dots which is in the green region, if we look carefully.

• There is no need to Scale the features as decision tree does not depends on Euclidean distance. We are using Feature Scaling here just to get a plot with better resolution. For example if in above case you ommit scaling then while ploting you will get MemoryError.

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