- Machine Learning (ML) based models are often black-box
- To use ML model in industrial setting it should be fair and reliable
- Metrics like accuracy-score or r2-score makes it reliable, but one cannot say anything about the fairness of the model
- ML engineer should be able to explain their models and understand the value and accuracy of their findings
- In this project, we have tried to interpret a trained ML model using CXPlain and SHAP library
- This project was developed for a #hackingforfuture Hackathon organized by International Center for Networked, Adaptive Production ICNAP in coorperation with the Fraunhofer Project Center at the University of Twente
- A ML model is trained on a tabular dataset for binary classification task
- Using this trained model, feature importance for the input features are calculated with the help of CXPlain and SHAP model interpretation library
- Results are compared quantitatively.
- cxplain 1.0.3
- shap 0.37.0
- pycaret 2.3.0
- tensorflow 2.4.1
- plotly 4.14.3
pip install cxplain
pip install shap
pip install pycaret
pip install tensorflow
pip install plotlyLight Gradient Boosting Machine Classification Report
Light Gradient Boosting Machine Confusion Matrix
Relative importance of features using CXPlain
