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sparse_encode — scikit-learn 1.7.2 documentation
n_nonzero_coefs = None , alpha = None , copy_cov = True , init = None ,...dictionary , * , gram = None , cov = None , algorithm = 'lasso_lars' ,...scikit-learn.org/stable/modules/generated/sklearn.decomposition.sparse_encode.html -
fastica — scikit-learn 1.7.2 documentation
fun = 'logcosh' , fun_args = None , max_iter = 200 , tol = 0.0001...n_components = None , * , algorithm = 'parallel' , whiten = 'unit-variance'...scikit-learn.org/stable/modules/generated/fastica-function.html -
SVM: Weighted samples — scikit-learn 1.7.2 docu...
n_features = 2 , n_informative = 2 , n_redundant = 0 , n_clusters_per_class...n_clusters_per_class = 1 , class_sep = 1.1 , weights = [ 0.9 , 0.1 ],...scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html -
Release Highlights for scikit-learn 1.5 — sciki...
n_features = 100 , tail_strength = 0.1 , random_state = 0 ) pca = PCA...random_state = 0 ) pca = PCA ( n_components = 10 , svd_solver = "covariance_eigh"...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html -
Isomap — scikit-learn 1.7.2 documentation
eigen_solver = 'auto' , tol = 0 , max_iter = None , path_method = 'auto'...neighbors_algorithm = 'auto' , n_jobs = None , metric = 'minkowski' , p = 2 ,...scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html -
Selecting dimensionality reduction with Pipelin...
LinearSVC(dual=False, max_iter=10000))]), n_jobs=1, param_grid=[{'classify__C':...LinearSVC X , y = load_digits ( return_X_y = True ) pipe = Pipeline...scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html -
Face completion with a multi-output estimators ...
targets = fetch_olivetti_faces ( return_X_y = True ) train = data...n_faces = 5 rng = check_random_state ( 4 ) face_ids = rng . randint...scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html -
Shrinkage covariance estimation: LedoitWolf vs ...
color = "magenta" , linewidth = 3 , label = "Ledoit-Wolf...loglik_oa , color = "purple" , linewidth = 3 , label = "OAS estimate"...scikit-learn.org/stable/auto_examples/covariance/plot_covariance_estimation.html -
LogisticRegressionCV — scikit-learn 1.7.2 docum...
Cs = 10 , fit_intercept = True , cv = None , dual = False...penalty = 'l2' , scoring = None , solver = 'lbfgs' , tol = 0.0001...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html -
Kernel PCA — scikit-learn 1.7.2 documentation
y = make_circles ( n_samples = 1_000 , factor = 0.3 , noise...test_ax ) = plt . subplots ( ncols = 2 , sharex = True , sharey...scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html