Gallery examples: Common pitfalls in the interpretation of coefficients of linear models| scikit-learn
Gallery examples: Recursive feature elimination with cross-validation GMM covariances Visualizing cross-validation behavior in scikit-learn Test with permutations the significance of a classificati...| scikit-learn
Gallery examples: Release Highlights for scikit-learn 1.4 Visualizing cross-validation behavior in scikit-learn| scikit-learn
Contains the metadata request info of a consumer.| scikit-learn
This guide demonstrates how metadata can be routed and passed between objects in scikit-learn. If you are developing a scikit-learn compatible estimator or meta-estimator, you can check our related...| scikit-learn
This glossary hopes to definitively represent the tacit and explicit conventions applied in Scikit-learn and its API, while providing a reference for users and contributors. It aims to describe the...| scikit-learn
Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would ha...| scikit-learn