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Version 0.23 — scikit-learn 1.7.2 documentation
previously didn’t work as documented – or according to reasonable...follow the Python logging documentation recommendation for libraries...scikit-learn.org/stable/whats_new/v0.23.html -
GridSearchCV — scikit-learn 1.7.2 documentation
best_estimator_ is defined (see the documentation for the refit parameter...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html -
Incremental PCA — scikit-learn 1.7.2 documentation
Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit in memory. IPCA build...scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html -
GMM covariances — scikit-learn 1.7.2 documentation
Demonstration of several covariances types for Gaussian mixture models. See Gaussian mixture models for more information on the estimator. Although GMM are often used for clustering, we can compare...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html -
Logistic function — scikit-learn 1.7.2 document...
Shown in the plot is how the logistic regression would, in this synthetic dataset, classify values as either 0 or 1, i.e. class one or two, using the logistic curve. Total running time of the scrip...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic.html -
Release History — scikit-learn 1.7.2 documentation
Changelogs and release notes for all scikit-learn releases are linked in this page. Version 1.7- Version 1.7.2, Version 1.7.1, Version 1.7.0., Version 1.6- Version 1.6.1, Version 1.6.0., Version 1....scikit-learn.org/stable/whats_new.html -
Kernel Approximation — scikit-learn 1.7.2 docum...
Examples concerning the sklearn.kernel_approximation module. Scalable learning with polynomial kernel approximationscikit-learn.org/stable/auto_examples/kernel_approximation/index.html -
Classifier comparison — scikit-learn 1.7.2 docu...
A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be take...scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html -
Feature discretization — scikit-learn 1.7.2 doc...
A demonstration of feature discretization on synthetic classification datasets. Feature discretization decomposes each feature into a set of bins, here equally distributed in width. The discrete va...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html -
Covariance estimation — scikit-learn 1.7.2 docu...
Examples concerning the sklearn.covariance module. Ledoit-Wolf vs OAS estimation Robust covariance estimation and Mahalanobis distances relevance Robust vs Empirical covariance estimate Shrinkage c...scikit-learn.org/stable/auto_examples/covariance/index.html