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RBFSampler — scikit-learn 1.7.1 documentation
1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...https://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf ) Examples >>>...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html -
ConstantKernel — scikit-learn 1.7.1 documentation
True ) (array([606.1]), array([0.248])) __call__ ( X , Y = None...constant_value = 1.0 , constant_value_bounds = (1e-05, 100000.0) ) [source]...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ConstantKernel.html -
cosine_similarity — scikit-learn 1.7.1 document...
= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 ,..., 1 , 0 ]] >>> cosine_similarity ( X , Y ) array([[0. , 0. ],...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html -
RocCurveDisplay — scikit-learn 1.7.1 documentation
0.1 , 0.4 , 0.35 , 0.8 ]) >>> fpr , tpr , thresholds = metrics...np . array ([ 0 , 0 , 1 , 1 ]) >>> y_score = np . array ([ 0.1...scikit-learn.org/stable/modules/generated/sklearn.metrics.RocCurveDisplay.html -
Perceptron — scikit-learn 1.7.1 documentation
0.001 , shuffle = True , verbose = 0 , eta0 = 1.0 , n_jobs =...penalty {‘l2’,’l1’,’elasticnet’}, default=None The penalty (aka regularization...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html -
cross_val_predict — scikit-learn 1.7.1 document...
instead. E.g.: cross_val_predict(..., params={'groups': groups})...‘2*n_jobs’ method {‘predict’, ‘predict_proba’, ‘predict_log_proba’,...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html -
precision_recall_curve — scikit-learn 1.7.1 doc...
66666667, 0.5 , 1. , 1. ]) >>> recall array([1. , 1. , 0.5, 0.5, 0....0. ]) >>> thresholds array([0.1 , 0.35, 0.4 , 0.8 ]) Gallery...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html -
median_absolute_error — scikit-learn 1.7.1 docu...
= [ 3 , - 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>..., 1 ], [ 7 , - 6 ]] >>> y_pred = [[ 0 , 2 ], [ - 1 , 2 ], [ 8...scikit-learn.org/stable/modules/generated/sklearn.metrics.median_absolute_error.html -
BernoulliNB — scikit-learn 1.7.1 documentation
size = ( 6 , 100 )) >>> Y = np . array ([ 1 , 2 , 3 , 4 , 4 , 5 ])...Press, pp. 234-265. https://nlp.stanford.edu/IR-book/html/html...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html -
SVR — scikit-learn 1.7.1 documentation
= 0.0 , tol = 0.001 , C = 1.0 , epsilon = 0.1 , shrinking = True...{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’...scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html