PCA
E97073
dimensionality reduction technique
machine learning algorithm
scikit-learn transformer
unsupervised learning method
PCA (Principal Component Analysis) in scikit-learn is a dimensionality reduction technique that transforms high-dimensional data into a smaller set of uncorrelated components capturing the most variance.
All labels observed (3)
| Label | Occurrences |
|---|---|
| principal component analysis | 2 |
| PCA canonical | 1 |
| Principal Component Analysis | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T816509 — 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: PCA Context triple: [scikit-learn, hasConcept, PCA]
-
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.
variational autoencoders
Variational autoencoders are a class of generative neural networks that learn probabilistic latent representations of data, enabling them to generate new, similar samples.
-
C.
PDL
PDL is a former name for USL League Two, a North American pre-professional soccer league that serves as a key development platform for college-aged and aspiring professional players.
-
D.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
E.
PA
PA is the standard two-letter U.S. Postal Service abbreviation for the state of Pennsylvania.
- 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: PCA Target entity description: PCA (Principal Component Analysis) in scikit-learn is a dimensionality reduction technique that transforms high-dimensional data into a smaller set of uncorrelated components capturing the most variance.
-
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.
variational autoencoders
Variational autoencoders are a class of generative neural networks that learn probabilistic latent representations of data, enabling them to generate new, similar samples.
-
C.
PDL
PDL is a former name for USL League Two, a North American pre-professional soccer league that serves as a key development platform for college-aged and aspiring professional players.
-
D.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
E.
PA
PA is the standard two-letter U.S. Postal Service abbreviation for the state of Pennsylvania.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
dimensionality reduction technique
ⓘ
machine learning algorithm ⓘ scikit-learn transformer ⓘ unsupervised learning method ⓘ |
| assumes | linear relationships in data ⓘ |
| basedOn |
Principal Component Analysis
ⓘ
linked to:
PCA
|
| captures | maximum variance directions ⓘ |
| commonlyUsedFor |
data visualization
ⓘ
feature extraction ⓘ noise reduction ⓘ |
| compatibleWith |
scikit-learn Pipeline
ⓘ
linked to:
scikit-learn
|
| hasAttribute |
components_
ⓘ
explained_variance_ ⓘ explained_variance_ratio_ ⓘ mean_ ⓘ n_components_ ⓘ n_features_in_ ⓘ noise_variance_ ⓘ singular_values_ ⓘ |
| implementedIn | Python ⓘ |
| inputShape | (n_samples, n_features) ⓘ |
| learnsFrom | covariance structure of the data ⓘ |
| modulePath | sklearn.decomposition.PCA ⓘ |
| outputShape | (n_samples, n_components) ⓘ |
| partOfLibrary | scikit-learn ⓘ |
| primaryGoal |
dimensionality reduction
ⓘ
variance maximization ⓘ |
| produces | uncorrelated components ⓘ |
| requires | numeric input data ⓘ |
| supportsMethod |
fit
ⓘ
fit_transform ⓘ get_params ⓘ inverse_transform ⓘ set_params ⓘ transform ⓘ |
| supportsParameter |
copy
ⓘ
dtype ⓘ iterated_power ⓘ n_components ⓘ random_state ⓘ svd_solver ⓘ tol ⓘ whiten ⓘ |
| svd_solverOption |
arpack
ⓘ
auto ⓘ full ⓘ randomized ⓘ |
| transforms | high-dimensional data ⓘ |
| uses | linear transformation ⓘ |
| whitenEffect | scales components to unit variance ⓘ |
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: PCA Description of subject: PCA (Principal Component Analysis) in scikit-learn is a dimensionality reduction technique that transforms high-dimensional data into a smaller set of uncorrelated components capturing the most variance.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: PCA
linked to: PCA