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