mstc.learning package

Submodules

mstc.learning.pipeline module

Components for cross-validation and model evaluation.

generate_cross_validation_pipeline(classifier, parameter_grid, folds=5, repeats=1, random_state=12345, number_of_jobs=1, scoring=None, refit=True)[source]

Evaluate a classifier trained with cross validation.

Parameters

  • classifier (sklearn.base.ClassifierMixin) – a classifier.

  • parameter_grid (dict) – grid of parameter.

  • folds (int) – number of stratified cross validation folds, defaults to 5.

  • repeats (int) – number of cross validation repeats, defaults to 1.

  • random_state (int) – random state, defaults to 12345.

  • number_of_jobs (int) – number of jobs to run in parallel, defaults to 1. -1 means using all processors.

  • scoring (string, callable, list/tuple, dict or None) – socring function or functions to evaluate predictions on the test set. Defaults to None to use the classifier default score method.

  • refit (bool, string) – whether to refit with best estimator. For multiple metric evaluation, this needs to be a string denoting the scorer is used to find the best parameters for refitting the estimator at the end.

Returns

an evaluation report.

Module contents