Towards Machine Learning Interpretability for Tabular Data with Mixed Data Types

Abstract

Gradient Boosting (GB) algorithms have been proposed for a variety of automated predictions and classification tasks with applications in many domains. These methods work faster and provide superior performance compared to deep learning methods when applied to tabular datasets. Another advantage is their interpretability. There are many machine learning methods that can train tabular data successfully, however, the inner workings are usually hidden from the user. In this context, SHAP values combined with GB methods, increase model transparency and provide not only consistent feature rankings but also show the contributions of the predictors for individual instances. In this work, we train multiple GB models using several tabular datasets and compare the result in terms of speed, performance, and the global and local models' interpretability.