An Explainable AutoML Pipeline for Multi-Task Tabular Data Using Optuna and SHAP
TL;DR
This research introduces an Explainable AutoML Pipeline for performing automated data pre-processing, multi-model learning, hyperparameter optimization using Optuna, model selection using a leaderboard, and finally interpreting the results using SHAP.
Abstract
Developing interpretable models on structured tabular data is difficult because there is a sequence dependency among data pre-processing, model learning, hyperparameter tuning, and result interpretation. The existing AutoML systems focus more on predictive accuracy rather than having an integrated approach towards explainability for different types of learning tasks. In this research, we introduce an Explainable AutoML Pipeline for performing automated data pre-processing, multi-model learning, hyperparameter optimization using Optuna, model selection using a leaderboard, and finally interpreting the results using SHAP. The framework automatically performs preprocessing on the input data, optimizes various candidate models, and determines the most suitable learning algorithm without any human intervention while also offering explanations about the decision-making process on a per-feature level. Model validation is then done by applying it to the California Housing and Titanic datasets. In particular, the optimized LightGBM model scored 0.8470 on the regression R2 metric, while the optimized XGBoost classifier managed to reach 81.56% accuracy, an F1-score of 0.7481, and an AUC of 0.8117. The SHAP analysis successfully detected the most influential predictive features, which increases the model interpretability