Feature selector is a tool for dimensionality reduction of machine learning datasets.
There are five methods used to identify features to remove:
- Missing Values
- Single Unique Values
- Collinear Features
- Zero Importance Features
- 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!