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. 2021 Nov 30;16(11):e0260395. doi: 10.1371/journal.pone.0260395

Fig 3. Classifications accuracy using digit frequency data.

Fig 3

Fabricated data was mixed with real data and given to four machine learning models for classification. Data shown represents 50 trials for 50 different fabricated dataset mixes. Features in this dataset are the digit frequencies for each sample. The red asterisk represents outliers in the boxplot; these same outliers are shown as normally colored points in jittered-point overlay. A. Results for data fabricated with the random method, mean classification accuracy: RF 100% (+/- 0%), SVM 100% (+/- 0%), GBC 100% (+/- 0%), NB 100% (+/- 0%), KNN 97% (+/- 1.3%). B. Results for data fabricated with the random method, mean classification F1: RF 1 (+/- 0), SVM 1 (+/- 0), GBC 1 (+/- 0), NB 1 (+/- 0), KNN 0.96 (+/- 0.02) C. Results for data fabricated with the resampling method, mean classification accuracy: RF 99% (+/- 0.8%), SVM 95% (+/- 2.3%), GBC 99% (+/- 1.7%), NB 96% (+/- 2.1%), KNN 85% (+/- 4.4%). D. Results for data fabricated with the resampled method, mean classification F1: RF 0.99 (+/- 0.01), SVM 0.94 (+/- 0.03), GBC 0.98 (+/- 0.02), NB 0.95 (+/- 0.03), KNN 0.82 (+/- 0.04) E. Results for data fabricated with the imputation method, mean classification accuracy: RF 100% (+/- 0%), SVM 100% (+/- 0.7%), GBC 100% (+/- 0%), NB 98% (+/- 0.7%), KNN 96% (+/- 1.5%). F. Results for data fabricated with the imputation method, mean classification F1: RF 1 (+/- 0), SVM 0.99 (+/- 0.01), GBC 1 (+/- 0), NB 0.97 (+/- 0.01), KNN 0.94 (+/- 0.02).