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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 -
SpectralClustering — scikit-learn 1.7.2 documen...
= 10 , gamma = 1.0 , affinity = 'rbf' , n_neighbors = 10 , eigen_tol.... exp ( - gamma * d ( X , X ) ** 2 ) or a k-nearest neighbors...scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html -
fetch_species_distributions — scikit-learn 1.7....
9833, -15.9 )], dtype=[('species', 'S22'), ('dd long', '<f4'), ('dd...-16.3333), (b'microryzomys_minutus', -67.8833, -16.3 ), (b'm...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_species_distributions.html -
ledoit_wolf — scikit-learn 1.7.2 documentation
ledoit_wolf ( X ) >>> covariance array([[0.44, 0.16], [0.16, 0.80]])...= np . array ([[ .4 , .2 ], [ .2 , .8 ]]) >>> rng = np . random...scikit-learn.org/stable/modules/generated/sklearn.covariance.ledoit_wolf.html -
GridSearchCV — scikit-learn 1.7.2 documentation
rank_t… ‘poly’ – 2 0.80 … 2 ‘poly’ – 3 0.70 … 4 ‘rbf’ 0.1 – 0.80 …...'param_gamma' : masked_array ( data = [ -- -- 0.1 0.2 ], mask...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.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 -
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 -
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 -
make_multilabel_classification — scikit-learn 1...
list ( y [: 3 ]) [array([1, 1, 0, 1, 0]), array([0, 1, 1, 1, 0]),...0]), array([0, 1, 0, 0, 0])] Gallery examples # Plot randomly generated...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_multilabel_classification.html -
DBSCAN — scikit-learn 1.7.2 documentation
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017)....= np . array ([[ 1 , 2 ], [ 2 , 2 ], [ 2 , 3 ], ... [ 8 , 7 ],...scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html