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StandardScaler — scikit-learn 1.7.1 documentation
transform ( data )) [[-1. -1.] [-1. -1.] [ 1. 1.] [ 1. 1.]] >>> print...>>> data = [[ 0 , 0 ], [ 0 , 0 ], [ 1 , 1 ], [ 1 , 1 ]] >>> scaler...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html -
make_pipeline — scikit-learn 1.7.1 documentation
Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb',...('gaussiannb', GaussianNB())]) Gallery examples # Time-related feature...scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html -
RBF — scikit-learn 1.7.1 documentation
[: 2 ,:]) array([[0.8354, 0.03228, 0.1322], [0.7906, 0.0652, 0.1441]])...load_iris ( return_X_y = True ) >>> kernel = 1.0 * RBF ( 1.0 ) >>>...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html -
d2_tweedie_score — scikit-learn 1.7.1 documenta...
y_true = [ 0.5 , 1 , 2.5 , 7 ] >>> y_pred = [ 1 , 1 , 5 , 3.5 ] >>>...than two. References [ 1 ] Eq. (3.11) of Hastie, Trevor J., Robert...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_tweedie_score.html -
label_ranking_average_precision_score — scikit-...
= np . array ([[ 0.75 , 0.5 , 1 ], [ 1 , 0.2 , 0.1 ]]) >>> l...y_true = np . array ([[ 1 , 0 , 0 ], [ 0 , 0 , 1 ]]) >>> y_score...scikit-learn.org/stable/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html -
d2_log_loss_score — scikit-learn 1.7.1 document...
of 0.0. Read more in the User Guide . Added in version 1.5. Parameters...sklearn.metrics. d2_log_loss_score ( y_true , y_pred , * , sample_weight...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_log_loss_score.html -
GaussianProcessRegressor — scikit-learn 1.7.1 d...
constant_value_bounds="fixed") * RBF(1.0, length_scale_bounds="fixed") is used...None , * , alpha = 1e-10 , optimizer = 'fmin_l_bfgs_b' , n_restarts_optimizer...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html -
FeatureHasher — scikit-learn 1.7.1 documentation
, 0., 0., 0., 2.], [ 0., 0., 0., -2., -5., 0., 0., 0., 0., 0.]])...transform ( D ) >>> f . toarray () array([[ 0., 0., -4., -1., 0., 0.,...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html -
make_blobs — scikit-learn 1.7.1 documentation
>>> y array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0]) >>> X , y = make_blobs...array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0]) Gallery examples # Probability...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html -
compute_optics_graph — scikit-learn 1.7.1 docum...
[‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’] from...‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’,...scikit-learn.org/stable/modules/generated/sklearn.cluster.compute_optics_graph.html