BaseSearchCV
E426667
BaseSearchCV is a scikit-learn base class that implements the core logic for hyperparameter search estimators, providing shared functionality for classes like GridSearchCV and RandomizedSearchCV.
All labels observed (1)
| Label | Occurrences |
|---|---|
| BaseSearchCV canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T4277037 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BaseSearchCV Context triple: [GridSearchCV, inheritsFrom, BaseSearchCV]
-
A.
GridSearchCV
GridSearchCV is a scikit-learn tool that systematically searches over specified hyperparameter values using cross-validation to find the best-performing model configuration.
-
B.
RandomizedSearchCV
RandomizedSearchCV is a scikit-learn tool that performs hyperparameter optimization by randomly sampling parameter combinations and evaluating them via cross-validation.
-
C.
Neural Architecture Search
Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.
-
D.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
E.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: BaseSearchCV Target entity description: BaseSearchCV is a scikit-learn base class that implements the core logic for hyperparameter search estimators, providing shared functionality for classes like GridSearchCV and RandomizedSearchCV.
-
A.
GridSearchCV
GridSearchCV is a scikit-learn tool that systematically searches over specified hyperparameter values using cross-validation to find the best-performing model configuration.
-
B.
RandomizedSearchCV
RandomizedSearchCV is a scikit-learn tool that performs hyperparameter optimization by randomly sampling parameter combinations and evaluating them via cross-validation.
-
C.
Neural Architecture Search
Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.
-
D.
scikit-learn
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
E.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
hyperparameter search base class
ⓘ
scikit-learn class ⓘ |
| designPattern | estimator API compatible ⓘ |
| documentationURL | https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.BaseSearchCV.html ⓘ |
| exposes | sklearn estimator interface via best_estimator_ after refit ⓘ |
| hasAttribute |
best_estimator_
ⓘ
best_index_ ⓘ best_params_ ⓘ best_score_ ⓘ cv ⓘ cv_results_ ⓘ error_score ⓘ estimator ⓘ iid (deprecated in newer versions) ⓘ n_jobs ⓘ n_splits_ ⓘ param_grid or param_distributions ⓘ pre_dispatch ⓘ refit ⓘ return_train_score ⓘ scorer_ ⓘ scoring ⓘ verbose ⓘ |
| hasMethod |
_run_search
ⓘ
decision_function ⓘ fit ⓘ inverse_transform ⓘ predict ⓘ predict_proba ⓘ score ⓘ transform ⓘ |
| hasPurpose | hyperparameter search over parameter grids or distributions ⓘ |
| implementedIn | Python NERFINISHED ⓘ |
| inheritsFrom |
sklearn.base.BaseEstimator
NERFINISHED
ⓘ
sklearn.base.MetaEstimatorMixin NERFINISHED ⓘ sklearn.model_selection._search.BaseSearchCV (internal module path) ⓘ |
| partOf | scikit-learn.model_selection module NERFINISHED ⓘ |
| providesFunctionalityFor |
GridSearchCV
NERFINISHED
ⓘ
RandomizedSearchCV NERFINISHED ⓘ |
| superclassOf |
GridSearchCV
NERFINISHED
ⓘ
RandomizedSearchCV NERFINISHED ⓘ |
| supports |
cross-validation
ⓘ
error_score handling ⓘ multiple scoring metrics ⓘ parallel computation via n_jobs ⓘ pre-dispatch of jobs ⓘ refit of best estimator ⓘ return_train_score option ⓘ verbose output ⓘ |
| usedBy |
hyperparameter tuning pipelines
ⓘ
model selection workflows ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: BaseSearchCV Description of subject: BaseSearchCV is a scikit-learn base class that implements the core logic for hyperparameter search estimators, providing shared functionality for classes like GridSearchCV and RandomizedSearchCV.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.