add random forest algorithm

decision_tree_classifier.py

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+from __future__ import division, print_function
+import numpy as np
+from sklearn import datasets
+import matplotlib.pyplot as plt
+import sys
+import os
+
+# Import helper functions
+from mlfromscratch.utils import train_test_split, standardize, accuracy_score
+from mlfromscratch.utils import mean_squared_error, calculate_variance, Plot
+from mlfromscratch.supervised_learning import ClassificationTree
+
+def main():
+
+    print ("-- Classification Tree --")
+
+    data = datasets.load_iris()
+    X = data.data
+    y = data.target
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
+
+    clf = ClassificationTree()
+    clf.fit(X_train, y_train)
+    y_pred = clf.predict(X_test)
+
+    accuracy = accuracy_score(y_test, y_pred)
+
+    print ("Accuracy:", accuracy)
+
+    Plot().plot_in_2d(X_test, y_pred, 
+        title="Decision Tree", 
+        accuracy=accuracy, 
+        legend_labels=data.target_names)
+
+
+if __name__ == "__main__":
+    main()

random_forest.py

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+from __future__ import division, print_function
+import numpy as np
+from sklearn import datasets
+from mlfromscratch.utils import train_test_split, accuracy_score, Plot
+from mlfromscratch.supervised_learning import RandomForest
+
+def main():
+    data = datasets.load_digits()
+    X = data.data
+    y = data.target
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=2)
+
+    clf = RandomForest(n_estimators=100)
+    clf.fit(X_train, y_train)
+    y_pred = clf.predict(X_test)
+
+    accuracy = accuracy_score(y_test, y_pred)
+
+    print ("Accuracy:", accuracy)
+
+    Plot().plot_in_2d(X_test, y_pred, title="Random Forest", accuracy=accuracy, legend_labels=data.target_names)
+
+
+if __name__ == "__main__":
+    main()