LogisticRegression
E97070
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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| LogisticRegression canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T816506 — 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: LogisticRegression Context triple: [scikit-learn, hasConcept, LogisticRegression]
-
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.
ROC
ROC is the commonly used abbreviation for the Royal Observer Corps, a former British civil defense organization that monitored aircraft and nuclear explosions during the 20th century.
-
C.
SGD
SGD is the official currency code for the Singapore dollar, the national currency of Singapore used in domestic and international transactions.
-
D.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
E.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
- 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: LogisticRegression Target entity description: 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.
-
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.
ROC
ROC is the commonly used abbreviation for the Royal Observer Corps, a former British civil defense organization that monitored aircraft and nuclear explosions during the 20th century.
-
C.
SGD
SGD is the official currency code for the Singapore dollar, the national currency of Singapore used in domestic and international transactions.
-
D.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
E.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
- F. None of above. chosen
Statements (60)
| Predicate | Object |
|---|---|
| instanceOf |
Python class
ⓘ
classification algorithm ⓘ linear model ⓘ scikit-learn estimator ⓘ |
| assumesRelationship | log-odds linear in features ⓘ |
| defaultMultiClass | auto ⓘ |
| defaultPenalty | l2 ⓘ |
| defaultSolver | lbfgs ⓘ |
| hasAttribute |
classes_
ⓘ
coef_ ⓘ intercept_ ⓘ |
| hasMethod |
decision_function
ⓘ
fit ⓘ predict ⓘ predict_proba ⓘ score ⓘ |
| linkFunctionFamily | logit link ⓘ |
| models | probability of class membership ⓘ |
| module |
sklearn.linear_model
ⓘ
linked to:
scikit-learn
|
| optimizationObjective | logistic loss minimization with regularization ⓘ |
| parameter |
C
ⓘ
class_weight ⓘ dual ⓘ fit_intercept ⓘ intercept_scaling ⓘ l1_ratio ⓘ max_iter ⓘ multi_class ⓘ n_jobs ⓘ penalty ⓘ random_state ⓘ solver ⓘ tol ⓘ verbose ⓘ warm_start ⓘ |
| providedBy | scikit-learn ⓘ |
| regularizationControlledBy | C ⓘ |
| requiresFeatureScaling | often beneficial ⓘ |
| supportsPenalty |
elasticnet
ⓘ
l1 ⓘ l2 ⓘ none ⓘ |
| supportsProbabilityEstimates | True ⓘ |
| supportsSolver |
lbfgs
ⓘ
liblinear ⓘ newton-cg ⓘ sag ⓘ saga ⓘ |
| supportsTask |
L1-regularized logistic regression
ⓘ
L2-regularized logistic regression ⓘ binary classification ⓘ elastic-net regularized logistic regression ⓘ multiclass classification ⓘ multinomial logistic regression ⓘ one-vs-one classification (via wrappers) ⓘ one-vs-rest classification ⓘ probability estimation ⓘ |
| usesDecisionBoundaryType | linear decision boundary ⓘ |
| usesLinkFunction |
logistic
ⓘ
sigmoid ⓘ |
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: LogisticRegression Description of subject: 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.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.