Principal Component Analysis
Principal Component Analysis (PCA) is a linear dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated components, ordered by how much variance in the original data they explain.
6 resources across 2 libraries
Glossary Terms(4)
Feature Selection
Feature selection is the process of choosing a subset of the most relevant input variables from a dataset to use in building a machine learning model, discardi…
Dimensionality Reduction
Dimensionality reduction is the process of transforming data with many input variables into a lower-dimensional representation that preserves as much meaningfu…
Principal Component Analysis
Principal Component Analysis (PCA) is a linear dimensionality reduction technique that transforms correlated variables into a smaller set of uncorrelated compo…
Autoencoder
An autoencoder is a type of neural network trained to reconstruct its own input by first compressing it into a smaller latent representation (encoding) and the…
Cheat Sheets(2)
Principal Component Analysis Cheat Sheet
A cheat sheet for Principal Component Analysis covering scikit-learn implementation, explained variance, choosing component counts, and reconstruction error.
Dimensionality Reduction Cheat Sheet
Techniques for reducing feature space while preserving structure, covering PCA, t-SNE, and UMAP with scikit-learn implementation examples.