33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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import pandas as pd
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import numpy as np
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pd.options.mode.chained_assignment = None
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FILE_PATH = "boston.csv"
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REQUIRED_COLUMNS = ['CRIM', 'DIS', 'TAX']
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TARGET_COLUMN = 'RAD'
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def print_classifier_info(feature_importance):
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feature_names = REQUIRED_COLUMNS
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embarked_score = feature_importance[-3:].sum()
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scores = np.append(feature_importance[:2], embarked_score)
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scores = map(lambda score: round(score, 2), scores)
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print(dict(zip(feature_names, scores)))
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if __name__ == '__main__':
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data = pd.read_csv(FILE_PATH)
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X = data[REQUIRED_COLUMNS]
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y = data[TARGET_COLUMN]
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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classifier_tree = DecisionTreeClassifier(random_state=42)
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classifier_tree.fit(X_train, y_train)
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print_classifier_info(classifier_tree.feature_importances_)
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print("Оценка качества (задача классификации) - ", classifier_tree.score(X_test, y_test))
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