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jaccard_score — scikit-learn 1.7.2 documentation
y_true = np . array ([[ 0 , 1 , 1 ], ... [ 1 , 1 , 0 ]]) >>> y_pred...y_pred = np . array ([[ 1 , 1 , 1 ], ... [ 1 , 0 , 0 ]]) In the binary...scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_score.html -
GaussianMixture — scikit-learn 1.7.2 documentation
covariance_type {‘full’, ‘tied’, ‘diag’, ‘spherical’}, default=’full’ String...‘k-means++’, ‘random’, ‘random_from_data’}, default=’kmeans’ The method...scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html -
LocallyLinearEmbedding — scikit-learn 1.7.2 doc...
{‘standard’, ‘hessian’, ‘modified’, ‘ltsa’}, default=’standard’ standard...distances. eigen_solver {‘auto’, ‘arpack’, ‘dense’}, default=’auto’...scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html -
PLSRegression — scikit-learn 1.7.2 documentation
= [[ 0. , 0. , 1. ], [ 1. , 0. , 0. ], [ 2. , 2. , 2. ], [ 2....2. , 5. , 4. ]] >>> y = [[ 0.1 , - 0.2 ], [ 0.9 , 1.1 ], [ 6.2...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html -
RFECV — scikit-learn 1.7.2 documentation
remove at each iteration. If within (0.0, 1.0), then step corresponds...rankings across (k)th fold. Selected (i.e., estimated best) features...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html -
SGDOneClassSVM — scikit-learn 1.7.2 documentation
directly). ‘constant’: eta = eta0 ‘optimal’: eta = 1.0 / (alpha...by np.random . learning_rate {‘constant’, ‘optimal’, ‘invscaling’,...scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html -
Hyperparameter — scikit-learn 1.7.2 documentation
... constant_value_bounds = ( 0.0 , 10.0 )) We can access each...bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) >>> params =...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html -
KernelRidge — scikit-learn 1.7.2 documentation
= 1.0 ) >>> krr . fit ( X , y ) KernelRidge(alpha=1.0) fit ( X...sklearn.kernel_ridge. KernelRidge ( alpha = 1 , * , kernel = 'linear' , gamma...scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html -
make_scorer — scikit-learn 1.7.2 documentation
LinearSVC (), param_grid = { 'C' : [ 1 , 10 ]}, ... scoring = ftwo_scorer...y_pred, **kwargs) . response_method {“predict_proba”, “decision_function”,...scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html -
PredictionErrorDisplay — scikit-learn 1.7.2 doc...
estimator , X , y , * , kind = 'residual_vs_predicted' , subsample = 1000...sklearn.metrics. PredictionErrorDispl ( * , y_true , y_pred ) [source]...scikit-learn.org/stable/modules/generated/sklearn.metrics.PredictionErrorDisplay.html