Transform data with a dimensionality-reduction model

These generics apply a fitted dimensionality-reduction mapping. They are distinct from predict(), which produces outcomes from supervised models and returns tidymodels-style prediction columns.

Usage

cuda_ml_transform(model, x, ...)

cuda_ml_inverse_transform(model, x, ...)

Arguments

model

A model object.

x

The dataset to be transformed.

...

Additional model-specific parameters (if any).

Value

cuda_ml_transform() returns coordinates in the learned representation. cuda_ml_inverse_transform() returns reconstructed predictors in the original feature space.

Supported methods

• cuda_ml_transform() maps predictors into a learned lower-dimensional representation. It supports fitted TSVD and UMAP models.

• cuda_ml_inverse_transform() maps component coordinates back toward the original feature space. It supports fitted PCA and TSVD models.

PCA stores the transformed training input when transform_input = TRUE, but it does not currently provide a method for transforming new data.

See also

predict, cuda_ml_pca, cuda_ml_tsvd, and cuda_ml_umap