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12. Dispatching — scikit-learn 1.7.2 docu...
Array API support (experimental)- Example usage, Support for Array API-compatible inputs, Input and output array type handling, Common estimator checks..scikit-learn.org/stable/dispatching.html -
Release Highlights — scikit-learn 1.7.2 d...
These examples illustrate the main features of the releases of scikit-learn. Release Highlights for scikit-learn 1.7 Release Highlights for scikit-learn 1.6 Release Highlights for scikit-learn 1.5 ...scikit-learn.org/stable/auto_examples/release_highlights/index.html -
Inductive Clustering — scikit-learn 1.7.2...
Clustering can be expensive, especially when our dataset contains millions of datapoints. Many clustering algorithms are not inductive and so cannot be directly applied to new data samples without ...scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.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 -
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.metrics — scikit-learn 1.7.2 docu...
Score functions, performance metrics, pairwise metrics and distance computations. User guide. See the Metrics and scoring: quantifying the quality of predictions and Pairwise metrics, Affinities an...scikit-learn.org/stable/api/sklearn.metrics.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.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 -
sklearn.exceptions — scikit-learn 1.7.2 d...
Custom warnings and errors used across scikit-learn.scikit-learn.org/stable/api/sklearn.exceptions.html