wehub-resource-sync
32 lines
826 B
Python
32 lines
826 B
Python
from sklearn.datasets import make_classification
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from sklearn.metrics import roc_auc_score
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from sklearn.model_selection import train_test_split
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from mla.naive_bayes import NaiveBayesClassifier
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def classification():
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# Generate a random binary classification problem.
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X, y = make_classification(
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n_samples=1000,
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n_features=10,
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n_informative=10,
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random_state=1111,
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n_classes=2,
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class_sep=2.5,
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n_redundant=0,
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)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.1, random_state=1111
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)
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model = NaiveBayesClassifier()
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)[:, 1]
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print("classification accuracy", roc_auc_score(y_test, predictions))
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if __name__ == "__main__":
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classification()
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