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plot_pca_iris.ipynb
projection=\"3d\", elev=-150, azim=110)\n\nX_reduced = PCA(n_c...PCA\n\nfig = plt.figure(1, figsize=(8, 6))\nax = fig.add_subplot(111,...scikit-learn.org/stable/_downloads/46b6a23d83637bf0f381ce9d8c528aa2/plot_pca_iris.ipynb -
LedoitWolf — scikit-learn 1.6.1 documentation
store_precision = True , assume_centered = False , block_size = 1000 )...], ... cov = real_cov , ... size = 50 ) >>> cov = LedoitWolf...scikit-learn.org/stable/modules/generated/sklearn.covariance.LedoitWolf.html -
FastICA — scikit-learn 1.6.1 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/sklearn.decomposition.FastICA.html -
Kernel Density Estimation — scikit-learn 1.6.1 ...
pca = PCA ( n_components = 15 , whiten = False ) data = pca ....new_data = kde . sample ( 44 , random_state = 0 ) new_data = pca ....scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html -
LeavePOut — scikit-learn 1.6.1 documentation
Train: index=[2 3] Test: index=[0 1] Fold 1: Train: index=[1 3] Test:...Test: index=[0 2] Fold 2: Train: index=[1 2] Test: index=[0 3] Fold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html -
k_means — scikit-learn 1.6.1 documentation
sample_weight = None , init = 'k-means++' , n_init = 'auto' , max_iter...max_iter = 300 , verbose = False , tol = 0.0001 , random_state...scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html -
ledoit_wolf — scikit-learn 1.6.1 documentation
>>> X = rng . multivariate_normal ( mean = [ 0 , 0 ], cov = real_cov...* , assume_centered = False , block_size = 1000 ) [source] # Estimate...scikit-learn.org/stable/modules/generated/sklearn.covariance.ledoit_wolf.html -
NuSVC — scikit-learn 1.6.1 documentation
nu = 0.5 , kernel = 'rbf' , degree = 3 , gamma = 'scale'...coef0 = 0.0 , shrinking = True , probability = False , tol = 0.001...scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html -
Recursive feature elimination with cross-valida...
n_informative = 3 , n_redundant = 2 , n_repeated = 0 , n_classes = 8 ,..., y = make_classification ( n_samples = 500 , n_features = 15...scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html -
Blind source separation using FastICA — scikit-...
n_samples = 2000 time = np . linspace ( 0 , 8 , n_samples ) s1 = np...( size = S . shape ) # Add noise S /= S . std ( axis = 0 ) # Standardize...scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html