RandomizedSearchCV
E97068
RandomizedSearchCV is a scikit-learn tool that performs hyperparameter optimization by randomly sampling parameter combinations and evaluating them via cross-validation.
Statements (42)
| Predicate | Object |
|---|---|
| instanceOf |
hyperparameter optimization tool
ⓘ
model selection utility ⓘ scikit-learn class ⓘ |
| advantage |
can find good configurations with fewer evaluations than grid search
ⓘ
explores large hyperparameter spaces efficiently ⓘ |
| canOptimize | any estimator with fit method ⓘ |
| definedInModule | sklearn.model_selection ⓘ |
| differsFrom | GridSearchCV by using random sampling instead of exhaustive search ⓘ |
| documentationURL | https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html ⓘ |
| hasAttribute |
best_estimator_
ⓘ
best_params_ ⓘ best_score_ ⓘ cv_results_ ⓘ n_splits_ ⓘ |
| hasParameter |
cv
ⓘ
error_score ⓘ estimator ⓘ iid ⓘ n_iter ⓘ n_jobs ⓘ param_distributions ⓘ pre_dispatch ⓘ random_state ⓘ refit ⓘ return_train_score ⓘ scoring ⓘ verbose ⓘ |
| inheritsFrom | BaseSearchCV ⓘ |
| introducedFor | model selection in scikit-learn ⓘ |
| language | Python ⓘ |
| license | BSD license (through scikit-learn) ⓘ |
| output | fitted estimator with best found hyperparameters ⓘ |
| partOf | scikit-learn ⓘ |
| performs | hyperparameter optimization ⓘ |
| requires | parameter distributions or lists in param_distributions ⓘ |
| samples | parameter combinations at random ⓘ |
| similarTo | GridSearchCV ⓘ |
| supports |
multiple scoring metrics via scoring parameter
ⓘ
parallel computation via n_jobs ⓘ randomized hyperparameter search ⓘ |
| typicalUseCase | tuning machine learning model hyperparameters ⓘ |
| uses | cross-validation ⓘ |
Referenced by (1)
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