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sklearn.model_selection — scikit-learn 1.8.0 do...
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.feature_selection — scikit-learn 1.8.0 ...
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.neural_network — scikit-learn 1.8.0 doc...
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.preprocessing — scikit-learn 1.8.0 docu...
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 -
12. Dispatching — scikit-learn 1.8.0 documentation
Array API support (experimental)- Enabling array API support, Example usage, Support for Array API-compatible inputs, Input and output array type handling, Common estimator checks..scikit-learn.org/stable/dispatching.html -
5. Inspection — scikit-learn 1.8.0 documentation
Predictive performance is often the main goal of developing machine learning models. Yet summarizing performance with an evaluation metric is often insufficient: it assumes that the evaluation metr...scikit-learn.org/stable/inspection.html -
sklearn.exceptions — scikit-learn 1.8.0 documen...
Custom warnings and errors used across scikit-learn.scikit-learn.org/stable/api/sklearn.exceptions.html -
sklearn.experimental — scikit-learn 1.8.0 docum...
Importable modules that enable the use of experimental features or estimators.scikit-learn.org/stable/api/sklearn.experimental.html -
sklearn.metrics — scikit-learn 1.8.0 documentation
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 -
1.13. Feature selection — scikit-learn 1.8.0 do...
The classes in the sklearn.feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy scores or to boost their perfor...scikit-learn.org/stable/modules/feature_selection.html