Fig. 5
From: Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes
Predictive ability under two sample sizes, 12k and 80k individuals, for 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 and mean squared error b. The 1000 causal QTN were distributed as clustered across the genome and gene action was a combination of additive, dominance and epistasis effects