Cross Validation Of Supervised Machine Learning Models Based On Random Forest and Support Vector Machine Techniques for 12S rRNA Molecular Marker: Implementation, Comparison and Utility
DOI:
https://doi.org/10.26438/ijcse/v6i11.345349Keywords:
Machine learning method, Random forest, Support vector machine, Folding level, 12S rRNA, Cross validationAbstract
Folding plays imperative role in the cross validation studies of machine learning based models. The folding divides the original sample into training and test sets, which evaluate performance of the machine learning based models and present scenarios for optimising the efficacy of such models. The present study discusses about the computational approaches applied for preparing training and test sets at different folds from 12S rRNA molecular marker sequence dataset of fish and application of these sets to estimate the performance of the proposed models based on machine learning techniques viz. Random Forest and Support Vector Machine. Additionally, the study presents the comparative accounts on efficacies of these models estimated at different folding. The findings from the study showed that folding has linear relationship with the efficacy of the model. The model with random forest was found better for solving the classification problems of the molecular marker sequence data. This study provides understanding on utility of the folding level in increasing the efficacy of the machine learning based methods and suggests for suitable machine learning method for solving the multiclass problem data especially where the identification using the molecular markers sequence data is involved
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