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February 2, 2026MathematicsOpen Access

Comparative Analysis and Optimisation of Machine Learning Models for Regression and Classification on Structured Tabular Datasets

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This research aims to compare and optimize machine learning models for regression and classification tasks on structured tabular datasets.
Conducted comparative analysis on various machine learning models including linear/logistic regression, SVMs, XGBoost, and MLPs.
Applied models across 72 datasets sourced from OpenML and Kaggle.
Evaluated dataset characteristics such as observation size, dimensionality, noise levels, linearity, and class balance.
Performed 576 experiments with two configurations per model to assess performance.
Found that understanding dataset characteristics is more critical than hyperparameter tuning.
Linear models consistently showed robust performance across different settings.
Non-linear models, such as XGBoost and MLP, were particularly effective in complex and noisy environments.

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Implication

Comparative analysis reveals insights for model selection in structured tabular data applications, highlighting the importance of dataset characteristics.

Cite This Study

Stumpfe et al. (2026) studied this question.

synapsesocial.com/papers/6980fd9dc1c9540dea80f53b https://doi.org/10.3390/math14030473

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Implication

Comparative analysis reveals insights for model selection in structured tabular data applications, highlighting the importance of dataset characteristics.

Comparative Analysis and Optimisation of Machine Learning Models for Regression and Classification on Structured Tabular Datasets

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Discussion

Implication

Cite This Study

Comparative Analysis and Optimisation of Machine Learning Models for Regression and Classification on Structured Tabular Datasets

Key Points

Discussion

Implication

Cite This Study

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