ArticleMolecules (Basel, Switzerland)2021
Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Optimizing Multi-Task Learning with Evolutionary Relatedness Metrics for Enhanced QSAR-Based Natural Product Activity Prediction.ACS omega · 2025Article
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- FMEA based prescriptive model for equipment repair guidance.Frontiers in artificial intelligence · 2025Article
- Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction.Computational and structural biotechnology journal · 2025Article
- Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology.Diagnostics (Basel, Switzerland) · 2024Article
- Modeling farmer behavior in adopting agroecological practices using AI tools in shea butter agroforestry systems.Heliyon · 2024Article
- Improvement of beam type element models of welded tubular junctions for fatigue analysis using artificial neural networks.Heliyon · 2024Article
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Authors and funding
3 authors.
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Abstract
Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size of the datasets and the train/test split ratios can greatly affect the outcome of the models, and thus the classification performance itself. We compared several combinations of dataset sizes and split ratios with five different machine learning algorithms to find the differences or similarities and to select the best parameter settings in nonbinary (multiclass) classification. It is also known that the models are ranked differently according to the performance merit(s) used. Here, 25 performance parameters were calculated for each model, then factorial ANOVA was applied to compare the results. The results clearly show the differences not just between the applied machine learning algorithms but also between the dataset sizes and to a lesser extent the train/test split ratios. The XGBoost algorithm could outperform the others, even in multiclass modeling. The performance parameters reacted differently to the change of the sample set size; some of them were much more sensitive to this factor than the others. Moreover, significant differences could be detected between train/test split ratios as well, exerting a great effect on the test validation of our models.
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