27 lines
883 B
Python
27 lines
883 B
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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path = "F1DriversDataset.csv"
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required = ['Pole_Positions', 'Race_Wins', 'Podiums']
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target = 'Championships'
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data = pd.read_csv(path)
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X = data[required]
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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=0.1, 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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feature_names = required
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embarked_score = classifier_tree.feature_importances_[-3:].sum()
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scores = np.append(classifier_tree.feature_importances_[: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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print("Оценка качества: ", classifier_tree.score(X_test, Y_test))
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