This example will demonstrate the set_output API to configure transformers to output pandas DataFrames. set_output can be configured per estimator by calling the set_output method or globally by se...| scikit-learn
In this example we illustrate text vectorization, which is the process of representing non-numerical input data (such as dictionaries or text documents) as vectors of real numbers. We first compare...| scikit-learn
Gallery examples: Column Transformer with Heterogeneous Data Sources FeatureHasher and DictVectorizer Comparison| 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
Gallery examples: Release Highlights for scikit-learn 1.5 Release Highlights for scikit-learn 1.4 Release Highlights for scikit-learn 1.1 Release Highlights for scikit-learn 1.0 Release Highlights ...| scikit-learn
The sklearn.feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. Loading featur...| 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