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.

Aliases (2)

PCA (scikit-learn) ×50
Principal Component Analysis ×1

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 →
captures	maximum variance directions →
commonlyUsedFor	data visualization → feature extraction → noise reduction →
compatibleWith	scikit-learn Pipeline →
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 NERFINISHED →
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 →

Referenced by (2)

Subject (surface form when different)	Predicate
PCA (scikit-learn) ("Principal Component Analysis") →	basedOn
scikit-learn →	hasConcept