cod3licious/autofeat
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
This helps data scientists and machine learning engineers build more accurate linear prediction models by automatically finding and creating new features from raw data. You input your existing dataset, and it outputs a refined set of features that improve your model's predictive power while keeping the model easy to understand. It's designed for professionals working with supervised learning tasks who need transparent, high-performing models.
537 stars. Used by 1 other package. Available on PyPI.
Use this if you need to improve the accuracy of a linear model and want to automatically discover complex relationships in your data without sacrificing model interpretability.
Not ideal if your primary goal is to use highly complex, non-linear models where interpretability is not a key concern, or if you need to manually control every aspect of feature generation.
Stars
537
Forks
64
Language
Python
License
MIT
Category
Last pushed
Jan 06, 2026
Commits (30d)
0
Dependencies
8
Reverse dependents
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/cod3licious/autofeat"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related frameworks
feature-engine/feature_engine
Feature engineering and selection open-source Python library compatible with sklearn.
alteryx/featuretools
An open source python library for automated feature engineering
abess-team/abess
Fast Best-Subset Selection Library
rodrigo-arenas/Sklearn-genetic-opt
ML hyperparameters tuning and features selection, using evolutionary algorithms.
abhayspawar/featexp
Feature exploration for supervised learning