- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 231 - 240 of 1,996 for = (0.14 sec)
-
make_sparse_spd_matrix — scikit-learn 1.7.0 doc...
make_sparse_spd_matrix ( n_dim = 1 , * , alpha = 0.95 , norm_diag = False , smallest_coef...smallest_coef = 0.1 , largest_coef = 0.9 , sparse_format = None , random_state...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html -
GaussianRandomProjection — scikit-learn 1.7.0 d...
GaussianRandomProjec ( n_components = 'auto' , * , eps = 0.1 , compute_inverse_components...compute_inverse_components = False , random_state = None ) [source] # Reduce...scikit-learn.org/stable/modules/generated/sklearn.random_projection.GaussianRandomProjection.html -
brier_score_loss — scikit-learn 1.7.0 documenta...
sample_weight = None , pos_label = None , labels = None , scale_by_half...default=None Label of the positive class when y_proba.shape = (n_samples,)...scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html -
1.8. Cross decomposition — scikit-learn 1.7.0 d...
transformed(X) = XU and transformed(Y) = YV . If n_components == 1 , PLSSVD...\(\Omega = \alpha \Xi\) . Then, we have \(Y = \Omega \Delta^T = \alpha...scikit-learn.org/stable/modules/cross_decomposition.html -
1.12. Multiclass and multioutput algorithms — s...
n_informative = 30 , n_classes = 3 , ... random_state = 1 ) >>> y2 = shuffle..., y = datasets . load_iris ( return_X_y = True ) >>> clf = OutputCodeClassifier...scikit-learn.org/stable/modules/multiclass.html -
Comparison of F-test and mutual information — s...
fontsize = 14 ) if i == 0 : plt . ylabel ( "$y$" , fontsize = 14 )...seed ( 0 ) X = np . random . rand ( 1000 , 3 ) y = X [:, 0 ] +...scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html -
PredefinedSplit — scikit-learn 1.7.0 documentation
Train: index=[1 2 3] Test: index=[0] Fold 1: Train: index=[0 2] Test:...get_n_splits ( X = None , y = None , groups = None ) [source]...scikit-learn.org/stable/modules/generated/sklearn.model_selection.PredefinedSplit.html -
glossary.rst.txt
_glossary: ========== Glossary of Common Terms and API Elements ==========...props`. General Concepts ========== .. glossary:: 1d 1d array...scikit-learn.org/stable/_sources/glossary.rst.txt -
OAS — scikit-learn 1.7.0 documentation
multivariate_normal ( mean = [ 0 , 0 ], ... cov = real_cov , ... size = 500 ) >>>...comp_cov , norm = 'frobenius' , scaling = True , squared = True ) [source]...scikit-learn.org/stable/modules/generated/sklearn.covariance.OAS.html -
make_low_rank_matrix — scikit-learn 1.7.0 docum...
n_samples = 100 , n_features = 100 , * , effective_rank = 10 , tail_strength...n_features = 25 , ... effective_rank = 5 , ... tail_strength = 0.01...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_low_rank_matrix.html