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check_cv — scikit-learn 1.5.2 documentation
Skip to main content Back to top Ctrl + K GitHub check_cv # sklearn.model_selection. check_cv ( cv = 5 , y = None , *...scikit-learn.org/stable/modules/generated/sklearn.model_selection.check_cv.html -
plot_tree — scikit-learn 1.5.2 documentation
Gallery examples: Plot the decision surface of decision trees trained on the iris dataset Understanding the decision tree structurescikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html -
sklearn.manifold — scikit-learn 1.5.2 documenta...
Data embedding techniques. User guide. See the Manifold learning section for further details.scikit-learn.org/stable/api/sklearn.manifold.html -
sklearn.multioutput — scikit-learn 1.5.2 docume...
Multioutput regression and classification. The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. The meta-estimator extends ...scikit-learn.org/stable/api/sklearn.multioutput.html -
sklearn.pipeline — scikit-learn 1.5.2 documenta...
Utilities to build a composite estimator as a chain of transforms and estimators. User guide. See the Pipelines and composite estimators section for further details.scikit-learn.org/stable/api/sklearn.pipeline.html -
Recursive feature elimination — scikit-learn 1....
This example demonstrates how Recursive Feature Elimination ( RFE) can be used to determine the importance of individual pixels for classifying handwritten digits. RFE recursively removes the least...scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html -
Pipelines and composite estimators — scikit-lea...
Examples of how to compose transformers and pipelines from other estimators. See the User Guide. Column Transformer with Heterogeneous Data Sources Column Transformer with Mixed Types Concatenating...scikit-learn.org/stable/auto_examples/compose/index.html -
sklearn.inspection — scikit-learn 1.5.2 documen...
Tools for model inspection. User guide. See the Inspection section for further details. Plotting:scikit-learn.org/stable/api/sklearn.inspection.html -
sklearn.ensemble — scikit-learn 1.5.2 documenta...
Ensemble-based methods for classification, regression and anomaly detection. User guide. See the Ensembles: Gradient boosting, random forests, bagging, voting, stacking section for further details.scikit-learn.org/stable/api/sklearn.ensemble.html -
sklearn.covariance — scikit-learn 1.5.2 documen...
Methods and algorithms to robustly estimate covariance. They estimate the covariance of features at given sets of points, as well as the precision matrix defined as the inverse of the covariance. C...scikit-learn.org/stable/api/sklearn.covariance.html