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examples/feature_selection
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#
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# We begin by loading the Breast Cancer dataset, consisting of 30 different
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# features and 569 samples.
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- from sklearn .datasets import load_breast_cancer
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import numpy as np
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+ from sklearn .datasets import load_breast_cancer
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+
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breast_cancer_data = load_breast_cancer ()
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X , y = breast_cancer_data .data , breast_cancer_data .target
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feature_names = np .array (breast_cancer_data .feature_names )
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# estimator with :class:`~sklearn.feature_selection.SequentialFeatureSelector`
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# to perform the feature selection.
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from sklearn .linear_model import LogisticRegression
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+ from sklearn .metrics import roc_auc_score
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from sklearn .pipeline import make_pipeline
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from sklearn .preprocessing import StandardScaler
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- from sklearn .metrics import roc_auc_score
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for tol in [- 1e-2 , - 1e-3 , - 1e-4 ]:
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start = time ()
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