Disambiguation evidence for PCA via surface form
"PCA (scikit-learn)"
As subject (50)
Triples where this entity appears as subject under the
label "PCA (scikit-learn)".
| Predicate | Object |
|---|---|
| assumes | linear relationships in data ⓘ |
| basedOn |
PCA
self-linksurface differs
ⓘ
surface form:
Principal Component Analysis
|
| captures | maximum variance directions ⓘ |
| commonlyUsedFor | data visualization ⓘ |
| commonlyUsedFor | feature extraction ⓘ |
| commonlyUsedFor | noise reduction ⓘ |
| compatibleWith |
scikit-learn
ⓘ
surface form:
scikit-learn Pipeline
|
| hasAttribute | components_ ⓘ |
| hasAttribute | explained_variance_ ⓘ |
| hasAttribute | explained_variance_ratio_ ⓘ |
| hasAttribute | mean_ ⓘ |
| hasAttribute | n_components_ ⓘ |
| hasAttribute | n_features_in_ ⓘ |
| hasAttribute | noise_variance_ ⓘ |
| hasAttribute | singular_values_ ⓘ |
| implementedIn | Python ⓘ |
| inputShape | (n_samples, n_features) ⓘ |
| instanceOf | dimensionality reduction technique ⓘ |
| instanceOf | machine learning algorithm ⓘ |
| instanceOf | scikit-learn transformer ⓘ |
| instanceOf | unsupervised learning method ⓘ |
| learnsFrom | covariance structure of the data ⓘ |
| modulePath | sklearn.decomposition.PCA ⓘ |
| outputShape | (n_samples, n_components) ⓘ |
| partOfLibrary | scikit-learn NERFINISHED ⓘ |
| primaryGoal | dimensionality reduction ⓘ |
| primaryGoal | variance maximization ⓘ |
| produces | uncorrelated components ⓘ |
| requires | numeric input data ⓘ |
| supportsMethod | fit ⓘ |
| supportsMethod | fit_transform ⓘ |
| supportsMethod | get_params ⓘ |
| supportsMethod | inverse_transform ⓘ |
| supportsMethod | set_params ⓘ |
| supportsMethod | transform ⓘ |
| supportsParameter | copy ⓘ |
| supportsParameter | dtype ⓘ |
| supportsParameter | iterated_power ⓘ |
| supportsParameter | n_components ⓘ |
| supportsParameter | random_state ⓘ |
| supportsParameter | svd_solver ⓘ |
| supportsParameter | tol ⓘ |
| supportsParameter | whiten ⓘ |
| svd_solverOption | arpack ⓘ |
| svd_solverOption | auto ⓘ |
| svd_solverOption | full ⓘ |
| svd_solverOption | randomized ⓘ |
| transforms | high-dimensional data ⓘ |
| uses | linear transformation ⓘ |
| whitenEffect | scales components to unit variance ⓘ |