Aug 2026· Journal of ISMAC· Vol 8, pp. 292-307· 0 citations· 14 references
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
Structured data drives enterprise decision-making, yet building predictive pipelines for time-series and tabular modalities requires intensive feature engineering, model selection, and other ''tricks of the trade''. AutoGluon, an open source AutoML system, automates this through multi-layer stack ensembling, providing a unified API that reliably achieves state-of-the-art accuracy across time series and tabular data. Concurrently, Foundation Models for Structured Data (FM4SD) have emerged to push the boundaries of predictive performance, enabling both powerful zero-shot inference and efficient fine-tuning. Specifically, Chronos-2 delivers state-of-the-art multivariate forecasting for time series, while a rapidly growing ecosystem of tabular foundation models (e.g., TabPFN, Mitra, TabICL) transforms how practitioners execute classification and regression tasks. This system-focused tutorial teaches attendees how to operationalize FM4SDs using AutoML across temporal and tabular modalities, with an additional highlight on the newly released Chronos-2. We guide participants through a technical progression: establishing AutoGluon as the orchestration framework, detailing the internal mechanics of Chronos-2 and various tabular FMs, and unifying them into production-ready pipelines.
Boran Han, Nick Erickson, Abdul Fatir Ansari et al.· Proceedings of the 32nd ACM...· 0 citations
Experimental evaluation on the Madelon dataset indicates that the automated and interpretable pipeline performs comparably to, and in some respects favourably against, baseline feature engineering approaches, demonstrating the practical effectiveness of combining scalable feature generation with interpretable AutoML.
Ms. CH. Vasavi, Ms. SK. Raqeeba· International Journal for Re...· 0 citations
Results support IAIML as a compact, interaction-aware framework appropriate for settings where bounded explanation size and controlled treatment of feature interactions are design requirements.
Not all training samples contribute equally to large language model fine-tuning. Selecting informative training samples can reduce the computational cost while preserving downstream performance. Many existing data selection methods rely on indirect heuristics, such as data quality, diversity or reasoning trace length. However, the effectiveness of these fixed criteria is task-dependent and difficult to generalize across diverse downstream tasks. Perplexity-based data selection provides a simple and model-aware solution to estimate the sample difficulty, but existing approaches typically score the entire training sequence and ignore the difference in learning objectives of language modeling and reasoning tasks. In this paper, we propose PPL-Factory, a simple and interpretable data selection framework that combines task-aware perplexity-based scores and data budget-aware selection criteria. Experiments on GSM8K demonstrate that PPL-Factory outperforms other state-of-the-art data selection methods using only $1\%$ of the training set. With $10\%$ of the data, PPL-Factory exceeds full-data fine-tuning accuracy by 0.9 on GSM8K and 4.8 on MATH. Overall, our results demonstrate that task-aware and budget-aware perplexity-based selection provides an effective and applicable approach for efficient fine-tuning.
BudgetDoc is introduced, the first multimodal benchmark providing explicit supervision for model-budget-performance trade-offs across three document tasks, and DRB (Document-Reasoning Balancer), an approx.