fig2

Figure 2. Workflow for the critical evaluation of forensic efficacy and kinship classification from the ML algorithm perspective. LR: Likelihood ratio; IBD: identity-by-descent; MLR: multinomial logistic regression; SVM: support vector machine; RFC: random forest classifier; ML: machine learning; LightGBM: light gradient boosting machine; PM: probability of matching; PD: power of discrimination; PE: probability of exclusion; PIC: polymorphism information content; TPI: typical paternity index; Ho: observed heterozygosity; He: expected heterozygosity; CPE: cumulative probability of exclusion; CPD: cumulative power of discrimination; Nei’s DA: Nei’s Genetic Distance; PCA: principal component analysis; t-SNE: t-distributed stochastic neighbor embedding; CV: cross-validation; ACC: accuracy; AUC: area under the curve; AMOVA: analysis of molecular variance; XGBoost: extreme gradient boosting.