|  | ## Principal Component Analysis

#### Synopsis

Performs a principal component analysis (PCA) using the covariance matrix.

#### Description

This operator performs a principal components analysis (PCA) using the covariance matrix. The user can specify the amount of variance to cover in the original data when retaining the best number of principal components. The user can also specify manually the number of principal components. The operator outputs a `PCAModel`. With the `ModelApplier` you can transform the features.

#### Input

- example set input: expects: ExampleSet

#### Output

- example set output:
- original:
- preprocessing model:

#### Parameters

- dimensionality reduction: Indicates which type of dimensionality reduction should be applied
- variance threshold: Keep the all components with a cumulative variance smaller than the given threshold.
- number of components: Keep this number of components.

#### ExampleProcess