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GMM Initialization Methods — scikit-learn...
subplot ( 2 , len ( methods ) // 2 , n + 1 ) start =...figsize = ( 4 * len ( methods ) // 2 , 6 )) plt . subplots_adjust (...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_init.html -
Model selection with Probabilistic PCA and Fact...
rand ( n_features ) + sigma / 2.0 X_hetero = X + rng . randn (...shrinkages = np . logspace ( - 2 , 0 , 30 ) cv = GridSearchCV (...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html -
Cross decomposition — scikit-learn 1.7.2 ...
Examples concerning the sklearn.cross_decomposition module. Compare cross decomposition methods Principal Component Regression vs Partial Least Squares Regressionscikit-learn.org/stable/auto_examples/cross_decomposition/index.html -
Related Projects — scikit-learn 1.7.2 doc...
Projects implementing the scikit-learn estimator API are encouraged to use the scikit-learn-contrib template which facilitates best practices for testing and documenting estimators. The scikit-lear...scikit-learn.org/stable/related_projects.html -
Frozen Estimators — scikit-learn 1.7.2 do...
Examples concerning the sklearn.frozen module. Examples of Using FrozenEstimatorscikit-learn.org/stable/auto_examples/frozen/index.html -
Nearest Neighbors — scikit-learn 1.7.2 do...
Examples concerning the sklearn.neighbors module. Approximate nearest neighbors in TSNE Caching nearest neighbors Comparing Nearest Neighbors with and without Neighborhood Components Analysis Dimen...scikit-learn.org/stable/auto_examples/neighbors/index.html -
sklearn.neural_network — scikit-learn 1.7...
Models based on neural networks. User guide. See the Neural network models (supervised) and Neural network models (unsupervised) sections for further details.scikit-learn.org/stable/api/sklearn.neural_network.html -
sklearn.feature_selection — scikit-learn ...
Feature selection algorithms. These include univariate filter selection methods and the recursive feature elimination algorithm. User guide. See the Feature selection section for further details.scikit-learn.org/stable/api/sklearn.feature_selection.html -
sklearn.preprocessing — scikit-learn 1.7....
Methods for scaling, centering, normalization, binarization, and more. User guide. See the Preprocessing data section for further details.scikit-learn.org/stable/api/sklearn.preprocessing.html -
sklearn.model_selection — scikit-learn 1....
Tools for model selection, such as cross validation and hyper-parameter tuning. User guide. See the Cross-validation: evaluating estimator performance, Tuning the hyper-parameters of an estimator, ...scikit-learn.org/stable/api/sklearn.model_selection.html