Fig. 3
From: Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes
Predictive ability of two conventional statistical methods (GBLUP and Bayes B) and four machine-learning methods including random forests (RF), gradient boosting (Boosting), multilayer perceptron (MLP) and convolutional neural network (CNN) using genotypes at causal loci. Predictive ability was evaluated using predictive correlation a, b and mean squared error c, d. Different numbers of causal QTN (100 or 1000) and two scenarios of gene action, namely additive and a combination of additive, dominance and epistasis were investigated. The QTN were distributed as clustered across the entire genome.