ColumnTransformer
E97069
ColumnTransformer is a scikit-learn utility that applies different preprocessing or transformation pipelines to specified columns of a dataset within a single unified estimator.
All labels observed (2)
| Label | Occurrences |
|---|---|
| ColumnTransformer canonical | 1 |
| sklearn.compose.ColumnTransformer | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T816501 — 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: ColumnTransformer Context triple: [scikit-learn, hasConcept, ColumnTransformer]
-
A.
COL
COL is the acronym for the Commonwealth of Learning, an intergovernmental organization that promotes open and distance learning across Commonwealth countries.
-
B.
COL$
COL$ is the currency symbol commonly used to denote the Colombian peso, the official monetary unit of Colombia.
-
C.
Progressive Field
Progressive Field is a Major League Baseball stadium in downtown Cleveland, Ohio, serving as the home ballpark of the Cleveland Guardians.
-
D.
Power Query
Power Query is a data connection and transformation tool used to import, clean, and reshape data from various sources before analysis in Microsoft Power BI and other Microsoft products.
-
E.
Power Pivot
Power Pivot is an Excel data modeling and analysis add-in that enables users to create sophisticated data models, relationships, and DAX calculations for business intelligence reporting.
- 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: ColumnTransformer Target entity description: ColumnTransformer is a scikit-learn utility that applies different preprocessing or transformation pipelines to specified columns of a dataset within a single unified estimator.
-
A.
COL
COL is the acronym for the Commonwealth of Learning, an intergovernmental organization that promotes open and distance learning across Commonwealth countries.
-
B.
COL$
COL$ is the currency symbol commonly used to denote the Colombian peso, the official monetary unit of Colombia.
-
C.
Progressive Field
Progressive Field is a Major League Baseball stadium in downtown Cleveland, Ohio, serving as the home ballpark of the Cleveland Guardians.
-
D.
Power Query
Power Query is a data connection and transformation tool used to import, clean, and reshape data from various sources before analysis in Microsoft Power BI and other Microsoft products.
-
E.
Power Pivot
Power Pivot is an Excel data modeling and analysis add-in that enables users to create sophisticated data models, relationships, and DAX calculations for business intelligence reporting.
- F. None of above. chosen
Statements (52)
| Predicate | Object |
|---|---|
| instanceOf |
estimator
ⓘ
scikit-learn class ⓘ transformer ⓘ |
| canContain |
Pipeline estimators
ⓘ
single transformers ⓘ |
| combinesOutputsBy | column-wise concatenation ⓘ |
| commonlyUsedWith |
OneHotEncoder
ⓘ
Pipeline ⓘ StandardScaler ⓘ |
| compatibleWith |
NumPy array
ⓘ
pandas DataFrame ⓘ scipy sparse matrix ⓘ |
| documentationURL | https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html ⓘ |
| hasFullName |
ColumnTransformer
self-linksurface differs
ⓘ
surface form:
sklearn.compose.ColumnTransformer
|
| hasParameter |
force_int_remainder_cols
ⓘ
n_jobs ⓘ remainder ⓘ sparse_threshold ⓘ transformer_weights ⓘ transformers ⓘ verbose ⓘ verbose_feature_names_out ⓘ |
| introducedInVersion |
scikit-learn
ⓘ
surface form:
scikit-learn 0.20
|
| outputType |
NumPy array
ⓘ
scipy sparse matrix ⓘ |
| parameterAllowedValue |
remainder='drop'
ⓘ
remainder='passthrough' ⓘ |
| parameterDefault | remainder='drop' ⓘ |
| parameterType |
force_int_remainder_cols:bool
ⓘ
n_jobs:int or None ⓘ remainder:str or estimator ⓘ sparse_threshold:float ⓘ transformer_weights:dict or None ⓘ transformers:list of tuples ⓘ verbose:bool ⓘ verbose_feature_names_out:bool or str ⓘ |
| partOf |
scikit-learn
ⓘ
sklearn.compose module ⓘ |
| supports | integration into scikit-learn Pipeline ⓘ |
| supportsColumnSelectionBy |
boolean masks
ⓘ
column indices ⓘ column names ⓘ |
| supportsMethod |
fit
ⓘ
fit_predict ⓘ fit_transform ⓘ get_feature_names_out ⓘ get_params ⓘ set_params ⓘ transform ⓘ |
| usedFor |
applying different transformers to different columns
ⓘ
column-wise preprocessing ⓘ heterogeneous feature preprocessing ⓘ |
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: ColumnTransformer Description of subject: ColumnTransformer is a scikit-learn utility that applies different preprocessing or transformation pipelines to specified columns of a dataset within a single unified estimator.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
sklearn.compose.ColumnTransformer