The dataset used in this example is The 20 newsgroups text dataset which will be automatically downloaded, cached and reused for the document classification example. In this example, we tune the hy...| scikit-learn
This example shows how to use cross_val_predict together with PredictionErrorDisplay to visualize prediction errors. We will load the diabetes dataset and create an instance of a linear regression ...| scikit-learn
A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features selected with cross-validation. Data generation: We build a classification task using 3 informative fea...| scikit-learn
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
Gallery examples: Release Highlights for scikit-learn 1.3 Model selection with Probabilistic PCA and Factor Analysis (FA) Lagged features for time series forecasting Imputing missing values before ...| 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
Gallery examples: Feature agglomeration vs. univariate selection Pipeline ANOVA SVM Recursive feature elimination Poisson regression and non-normal loss Permutation Importance vs Random Forest Feat...| scikit-learn