Skip to main content

PCA with varimax rotation and feature selection compatible with scikit-learn

Project description

Researchers use Principle Component Analysis (PCA) intending to summarize features, identify structure in data or reduce the number of features. The interpretation of principal components is challenging in most of the cases due to the high amount of cross-loadings (one feature having significant weight across many principal components). Different types of matrix rotations are used to minimize cross-loadings and make factor interpretation easier.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

smart_pca-0.1.2.tar.gz (8.1 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page