feature-selector

Development and implementation of the feature selector

The feature selector is a class for removing features for a dataset intended for machine learning. 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

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

Refer to the Feature Selector Usage notebook for how to use

Requires:

lightgbm==2.1.1
numpy==1.14.5
pandas==0.23.1
scikit-learn==0.19.1