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make_blobs — scikit-learn 1.5.2 documentation
2) >>> y array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0]) Gallery...shape ) (10, 2) >>> y array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0]) >>>...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html -
accuracy_score — scikit-learn 1.5.2 documentation
2 , 1 , 3 ] >>> y_true = [ 0 , 1 , 2 , 3 ] >>> accuracy_score...], [ 1 , 1 ]]), np . ones (( 2 , 2 ))) 0.5 Gallery examples # Plot...scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html -
label_binarize — scikit-learn 1.5.2 documentation
2 , 4 , 6 ]) array([[1, 0, 0, 0],...6 ], classes = [ 1 , 6 , 4 , 2 ]) array([[1, 0, 0, 0], [0, 1,...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.label_binarize.html -
FeatureAgglomeration — scikit-learn 1.5.2 docum...
FeatureAgglomeration ( n_clusters=2 , * , metric='euclidean' , memory=None...n_clusters int or None, default=2 The number of clusters to find....scikit-learn.org/stable/modules/generated/sklearn.cluster.FeatureAgglomeration.html -
f1_score — scikit-learn 1.5.2 documentation
2 , 0 , 1 , 2 ] >>> y_pred = [ 0 , 2 , 1 , 0 , 0...is: \[\text{F1} = \frac{2 * \text{TP}}{2 * \text{TP} + \text{FP}...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html -
WhiteKernel — scikit-learn 1.5.2 documentation
x_2) = noise\_level \text{ if } x_i...0.3680... >>> gpr . predict ( X [: 2 ,:], return_std = True ) (array([653.0...,...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.WhiteKernel.html -
adjusted_mutual_info_score — scikit-learn 1.5.2...
JMLR [ 2 ] Wikipedia entry for the Adjusted...([ 0 , 0 , 0 , 0 ], [ 0 , 1 , 2 , 3 ]) ... 0.0 Gallery examples...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html -
2.6. Covariance estimation — scikit-learn 1.5.2...
2.6.2. Shrunk Covariance # 2.6.2.1. Basic shrinkage...ShrunkCovariance object to data. 2.6.2.2. Ledoit-Wolf shrinkage # In...scikit-learn.org/stable/modules/covariance.html -
Generalized Linear Models — scikit-learn 1.5.2 ...
scikit-learn.org/stable/auto_examples/linear_model/index.html -
Elastic License 2.0 | Elastic
Elastic License AcceptanceBy using the software, you agree to all of the terms and conditions below. Copyright LicenseThe licensor grants you a non-exclusive, royalty-free, worldwide, non-sublicensab...www.elastic.co/licensing/elastic-license