Tinny-Robot/DimSense
DimSense: Empower your machine learning projects with advanced feature selection and extraction techniques. Streamline dimensionality reduction and boost model performance. Your go-to toolkit for intelligent data dimension management.
This library helps machine learning developers refine their datasets by selecting the most important features or creating more concise representations. You input your raw tabular data and target variable, and it outputs a refined dataset with reduced complexity. It's for anyone building machine learning models who needs to improve performance or manage large datasets.
No commits in the last 6 months. Available on PyPI.
Use this if you are a machine learning engineer or data scientist looking to optimize your model's accuracy and training time by intelligently reducing the number of input variables.
Not ideal if you are looking for a no-code solution or a tool that automatically handles the entire machine learning pipeline beyond feature management.
Stars
13
Forks
6
Language
Python
License
MIT
Category
Last pushed
Jan 28, 2024
Commits (30d)
0
Dependencies
5
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Tinny-Robot/DimSense"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
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
cod3licious/autofeat
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
abess-team/abess
Fast Best-Subset Selection Library
abhayspawar/featexp
Feature exploration for supervised learning