This examples shows how a classifier is optimized by cross-validation, which is done using the GridSearchCV object on a development set that comprises only half of the available labeled data. The p...| 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: Feature agglomeration vs. univariate selection Comparing Random Forests and Histogram Gradient Boosting models Gradient Boosting Out-of-Bag estimates Visualizing cross-validation ...| 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
Change the default backend used by Parallel inside a with block.| joblib.readthedocs.io
Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of the estimator classes. Typical examples include C,...| 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
There are 3 different APIs for evaluating the quality of a model’s predictions: Estimator score method: Estimators have a score method providing a default evaluation criterion for the problem they ...| scikit-learn