SVC
E97071
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
All labels observed (1)
| Label | Occurrences |
|---|---|
| SVC canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T816507 — 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: SVC Context triple: [scikit-learn, hasConcept, SVC]
-
A.
SVR
SVR is the set of post-nominal letters used to denote recipients of the Order of the White Rose of Finland.
-
B.
SVR
SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
-
C.
SCC
SCC is the commonly used abbreviation for the MIT Schwarzman College of Computing, an interdisciplinary hub for computing and AI research and education.
-
D.
SCC
SCC is the commonly used abbreviation for the Supreme Court of Canada, the country's highest judicial authority.
-
E.
CHED
CHED is the commonly used abbreviation for the Division of Chemical Education, a professional organization focused on advancing the teaching and learning of chemistry.
- 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: SVC Target entity description: SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
-
A.
SVR
SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
-
B.
SVR
SVR is the set of post-nominal letters used to denote recipients of the Order of the White Rose of Finland.
-
C.
SCC
SCC is the commonly used abbreviation for the Supreme Court of Canada, the country's highest judicial authority.
-
D.
SCC
SCC is the commonly used abbreviation for the MIT Schwarzman College of Computing, an interdisciplinary hub for computing and AI research and education.
-
E.
CHED
CHED is the commonly used abbreviation for the Division of Chemical Education, a professional organization focused on advancing the teaching and learning of chemistry.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
Python class
ⓘ
Support Vector Machine classifier ⓘ machine learning model class ⓘ scikit-learn estimator ⓘ |
| basedOn |
Support Vector Machines
ⓘ
surface form:
C-Support Vector Classification
Support Vector Machines ⓘ |
| belongsToEcosystem |
Python scientific stack
ⓘ
surface form:
Python scientific computing stack
|
| defaultKernel | rbf ⓘ |
| handles | non-linearly separable data ⓘ |
| hyperparameter |
C
ⓘ
break_ties ⓘ cache_size ⓘ class_weight ⓘ coef0 ⓘ decision_function_shape ⓘ degree ⓘ gamma ⓘ kernel ⓘ max_iter ⓘ probability ⓘ random_state ⓘ shrinking ⓘ tol ⓘ verbose ⓘ |
| implementedInLibrary | scikit-learn ⓘ |
| method |
decision_function
ⓘ
fit ⓘ predict ⓘ predict_proba ⓘ score ⓘ |
| module | sklearn.svm ⓘ |
| optimizationSolver | libsvm ⓘ |
| outputType | class labels ⓘ |
| probabilisticOutput | predict_proba ⓘ |
| regularizationParameter | C ⓘ |
| supports |
kernel methods
ⓘ
non-linear decision boundaries ⓘ one-vs-one multiclass strategy ⓘ probability estimates via Platt scaling ⓘ sample weights ⓘ sparse input ⓘ |
| supportsKernel |
linear
ⓘ
poly ⓘ precomputed ⓘ rbf ⓘ sigmoid ⓘ |
| usedFor |
binary classification
ⓘ
multiclass classification ⓘ supervised learning ⓘ |
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: SVC Description of subject: SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.