Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification.

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 c

SUBMITTER: Racz A 

PROVIDER: S-EPMC7922354 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature