FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
https://doi.org/10.1109/TVCG.2022.3141040
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12 lines
404 B
12 lines
404 B
4 years ago
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# first line: 447
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@memory.cache
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def create_global_function():
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global estimator
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def estimator(C, gamma):
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# initialize model
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model = SVC(C=C, gamma=gamma, degree=1, random_state=RANDOM_SEED)
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# set in cross-validation
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result = cross_validate(model, XData, yData, cv=crossValidation)
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# result is mean of test_score
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return np.mean(result['test_score'])
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