Tools for model selection, such as cross validation and hyper-parameter tuning.
User guide. See the Cross-validation: evaluating estimator performance, Tuning the hyper-parameters of an estimator, and Learning curve sections for further details.
Splitters#K-fold iterator variant with non-overlapping groups.
Shuffle-Group(s)-Out cross-validation iterator.
K-Fold cross-validator.
Leave One Group Out cross-validator.
Leave-One-Out cross-validator.
Leave P Group(s) Out cross-validator.
Leave-P-Out cross-validator.
Predefined split cross-validator.
Repeated K-Fold cross validator.
Repeated class-wise stratified K-Fold cross validator.
Random permutation cross-validator.
Class-wise stratified K-Fold iterator variant with non-overlapping groups.
Class-wise stratified K-Fold cross-validator.
Class-wise stratified ShuffleSplit cross-validator.
Time Series cross-validator.
Input checker utility for building a cross-validator.
Split arrays or matrices into random train and test subsets.
Hyper-parameter optimizers#Exhaustive search over specified parameter values for an estimator.
Search over specified parameter values with successive halving.
Randomized search on hyper parameters.
Grid of parameters with a discrete number of values for each.
Generator on parameters sampled from given distributions.
Randomized search on hyper parameters.
Post-fit model tuning#Binary classifier that manually sets the decision threshold.
Classifier that post-tunes the decision threshold using cross-validation.
Model validation#Generate cross-validated estimates for each input data point.
Evaluate a score by cross-validation.
Evaluate metric(s) by cross-validation and also record fit/score times.
Learning curve.
Evaluate the significance of a cross-validated score with permutations.
Validation curve.
Visualization#Learning Curve visualization.
Validation Curve visualization.
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