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Feature discretization — scikit-learn 1.7...
logspace ( - 1 , 1 , 3 )}, ), ( make_pipeline (..."linearsvc__C" : np . logspace ( - 1 , 1 , 3 )}, ), ( make_pipeline (...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html -
Classifier comparison — scikit-learn 1.7....
C = 1 , random_state = 42 ), GaussianProcessClass ( 1.0 * RBF...max_features = 1 , random_state = 42 ), MLPClassifier ( alpha = 1 , max_iter...scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html -
GMM covariances — scikit-learn 1.7.2 docu...
shape [ 1 ]) * gmm . covariances_ [ n ]...]) angle = np . arctan2 ( u [ 1 ], u [ 0 ]) angle = 180 * angle...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html -
Incremental PCA — scikit-learn 1.7.2 docu...
scatterpoints = 1 ) plt . axis ([ - 4 , 4 , - 1.5 , 1.5 ]) plt . show...target_name in zip ( colors , [ 0 , 1 , 2 ], iris . target_names ):...scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html -
Logistic function — scikit-learn 1.7.2 do...
1 ]) plt . ylim ( - 0.25 , 1.25 ) plt . xlim...classify values as either 0 or 1, i.e. class one or two, using...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic.html -
2.6. Covariance estimation — scikit-learn...
1. Empirical covariance # The covariance...2.6.2. Shrunk Covariance # 2.6.2.1. Basic shrinkage # Despite being...scikit-learn.org/stable/modules/covariance.html -
Isotonic Regression — scikit-learn 1.7.2 ...
An illustration of the isotonic regression on generated data (non-linear monotonic trend with homoscedastic uniform noise). The isotonic regression algorithm finds a non-decreasing approximation of...scikit-learn.org/stable/auto_examples/miscellaneous/plot_isotonic_regression.html -
sklearn.cluster — scikit-learn 1.7.2 docu...
Popular unsupervised clustering algorithms. User guide. See the Clustering and Biclustering sections for further details.scikit-learn.org/stable/api/sklearn.cluster.html -
sklearn.decomposition — scikit-learn 1.7....
Matrix decomposition algorithms. These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be regarded as dimensionality reduction techniques. User guide. See the Decomposing...scikit-learn.org/stable/api/sklearn.decomposition.html -
Model Selection — scikit-learn 1.7.2 docu...
Examples related to the sklearn.model_selection module. Balance model complexity and cross-validated score Class Likelihood Ratios to measure classification performance Comparing randomized search ...scikit-learn.org/stable/auto_examples/model_selection/index.html