Introduction Dimensionality reduction can be a critical preprocessing step that transforms a dataset’s features with high dimensions in input space to much lower dimensions in some latent space. It can bring us multiple benefits when training the model including avoiding the curse of dimensionality issues, reducing the risk of model overfitting, and lowering the computation […]
How to Evaluate Features after Dimensionality Reduction?
