Principal Component Analysis (PCA) Methods in Scikit-Learn with Python

There are 11 main methods that can be used when working with a principal component analysis object in Python.

Here is a table listing all the methods that can be applied on the PCA object.

fit()Fit the model with X.
fit_transform()Fit the model with X and apply the dimensionality reduction on X.
get_covariance()Compute data covariance with the generative model.
get_feature_names_out()Get output feature names for transformation.
get_params()Get parameters for this estimator.
get_precision()Compute data precision matrix with the generative model.
inverse_transform()Transform data back to its original space.
score()Return the average log-likelihood of all samples.
score_samples()Return the log-likelihood of each sample.
set_params()Set the parameters of this estimator.
transform()Apply dimensionality reduction to X.

to learn more, read our tutorial on how to use PCA with Python.

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