GridSearchCV
E97067
GridSearchCV is a scikit-learn tool that systematically searches over specified hyperparameter values using cross-validation to find the best-performing model configuration.
All labels observed (1)
| Label | Occurrences |
|---|---|
| GridSearchCV canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T816497 — 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: GridSearchCV Context triple: [scikit-learn, hasConcept, GridSearchCV]
-
A.
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.
-
B.
RLlib
RLlib is a scalable, open-source reinforcement learning library built on Ray that provides high-level APIs and distributed training support for a wide range of RL algorithms.
-
C.
Keras
Keras is a high-level neural networks API written in Python that simplifies building, training, and deploying deep learning models, often running on top of frameworks like TensorFlow.
-
D.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
E.
SciPy
SciPy is an open-source Python library that provides advanced scientific and technical computing tools, including modules for optimization, integration, statistics, signal processing, and linear algebra.
- 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: GridSearchCV Target entity description: GridSearchCV is a scikit-learn tool that systematically searches over specified hyperparameter values using cross-validation to find the best-performing model configuration.
-
A.
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.
-
B.
RLlib
RLlib is a scalable, open-source reinforcement learning library built on Ray that provides high-level APIs and distributed training support for a wide range of RL algorithms.
-
C.
Keras
Keras is a high-level neural networks API written in Python that simplifies building, training, and deploying deep learning models, often running on top of frameworks like TensorFlow.
-
D.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
E.
SciPy
SciPy is an open-source Python library that provides advanced scientific and technical computing tools, including modules for optimization, integration, statistics, signal processing, and linear algebra.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
hyperparameter optimization tool
ⓘ
model selection utility ⓘ scikit-learn class ⓘ |
| acceptsParameter |
cv
ⓘ
error_score ⓘ estimator ⓘ iid ⓘ n_jobs ⓘ param_grid ⓘ pre_dispatch ⓘ refit ⓘ return_train_score ⓘ scoring ⓘ verbose ⓘ |
| compatibleWith | any scikit-learn estimator with fit method ⓘ |
| definedInModule | sklearn.model_selection ⓘ |
| documentedAt | https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html ⓘ |
| hasAttribute |
best_estimator_
ⓘ
best_index_ ⓘ best_params_ ⓘ best_score_ ⓘ cv_results_ ⓘ n_splits_ ⓘ scorer_ ⓘ |
| hasMethod |
fit
ⓘ
get_params ⓘ predict ⓘ score ⓘ set_params ⓘ |
| inheritsFrom | BaseSearchCV ⓘ |
| introducedInLibrary |
scikit-learn
ⓘ
surface form:
scikit-learn 0.16 or earlier
|
| parallelization | uses joblib for parallel computation ⓘ |
| parameterType |
cv can be cross-validation splitter
ⓘ
cv can be int ⓘ n_jobs can be -1 for using all processors ⓘ param_grid can be dict ⓘ param_grid can be list of dicts ⓘ scoring can be callable ⓘ scoring can be string ⓘ |
| partOf | scikit-learn ⓘ |
| primaryPurpose |
hyperparameter tuning
ⓘ
model selection ⓘ |
| refitBehavior | refits best_estimator_ on full training data when refit=True ⓘ |
| searchStrategy | exhaustive grid search ⓘ |
| selectionCriterion | maximizes scoring metric on validation folds ⓘ |
| supports |
classification
ⓘ
clustering if estimator supports scoring ⓘ regression ⓘ |
| supportsLanguage | Python ⓘ |
| usesTechnique | cross-validation ⓘ |
| writtenInLanguage | Python ⓘ |
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: GridSearchCV Description of subject: GridSearchCV is a scikit-learn tool that systematically searches over specified hyperparameter values using cross-validation to find the best-performing model configuration.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.