How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Feature selector is a tool for dimensionality reduction of machine learning datasets.
The main features of willkoehrsen/feature-selector are: Feature Engineering Tools.
Projects with overlapping indexed features include: alteryx/featuretools — Featuretools is an automated feature engineering library and data transformation framework written in Python. It… cokelaer/fitter — ############################# FITTER documentation #############################. maxhalford/prince — :crown: Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA. scikit-learn-contrib/imbalanced-learn — imbalanced-learn is a dataset balancing framework and Python machine learning extension designed to resample training…
Featuretools is an automated feature engineering library and data transformation framework written in Python. It automatically generates machine learning feature vectors from multi-table datasets by applying synthesis patterns to relational and timestamped data. The system functions as a distributed feature synthesis engine, allowing the process of creating feature vectors to scale across multiple cores or clusters to handle large-scale datasets. The library supports the synthesis of multi-table datasets, time series feature generation, and the creation of custom machine learning primitives
############################# FITTER documentation #############################
:crown: Multivariate exploratory data analysis in Python — PCA, CA, MCA, MFA, FAMD, GPA
imbalanced-learn is a dataset balancing framework and Python machine learning extension designed to resample training data and reduce the impact of class imbalance. It provides a toolkit of algorithms for adjusting class distributions to improve model performance on minority class prediction. As a scikit-learn resampling library, it extends the ecosystem with specialized tools for balancing datasets through over-sampling and under-sampling techniques. This allows for the correction of skewed class proportions to reduce model bias toward the majority class. The library implements the scikit-l