Various utilities to help with development.
Developer guide. See the Utilities for Developers section for further details.
Input and parameter validation#Functions to validate input and parameters within scikit-learn estimators.
Meta-estimators#Utilities for meta-estimators.
Weight handling based on class labels#Utilities for handling weights based on class labels.
Dealing with multiclass target in classifiers#Utilities to handle multiclass/multioutput target in classifiers.
Optimal mathematical operations#Utilities to perform optimal mathematical operations in scikit-learn.
Working with sparse matrices and arrays#A collection of utilities to work with sparse matrices and arrays.
Utilities to work with sparse matrices and arrays written in Cython.
Working with graphs#Graph utilities and algorithms.
Random sampling#Utilities for random sampling.
Auxiliary functions that operate on arrays#A small collection of auxiliary functions that operate on arrays.
Metadata routing#Utilities to route metadata within scikit-learn estimators.
User guide. See the Metadata Routing section for further details.
Discovering scikit-learn objects#Utilities to discover scikit-learn objects.
API compatibility checkers#Various utilities to check the compatibility of estimators with scikit-learn API.
Parallel computing#Customizations of joblib and threadpoolctl tools for scikit-learn usage.
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