Feature selector is a tool for dimensionality reduction of machine learning datasets.

There are five methods used to identify features to remove:

1. Missing Values
2. Single Unique Values
3. Collinear Features
4. Zero Importance Features
5. Low Importance Features

Refer to the Feature Selector Usage notebook for how to use

The FeatureSelector also includes a number of visualization methods to inspect characteristics of a dataset.

Correlation Heatmap

Most Important Features

Requires:

python==3.6+
lightgbm==2.1.1
matplotlib==2.1.2
seaborn==0.8.1
numpy==1.22.0
pandas==0.23.1
scikit-learn==0.19.1

Any questions can be directed to wjk68@case.edu!