Skip to main content

Table 2 Effective number of parameters, predictive correlations, and mean squared errors of prediction: wheat.1

From: Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat

ANN architectures

Linear

1 neuron

2 neurons

3 neurons

4 neurons

Criterion

     

Effective number of parameters

299 ± 5.5

260 ± 6.1

253 ± 5.9

238 ± 5.5

220 ± 2.8

Correlations in testing set

0.48 ± 0.03

0.54 ± 0.03

056 ± 0.02

0.57 ± 0.02

0.59 ± 0.02

Mean squared error in testing set

0.99 ± 0.04

0.77 ± 0.03

0.74 ± 0.03

0.71 ± 0.02

0.72 ± 0.02

  1. 1 The training-test partitions for this data were random and repeated 50 times; standard errors in parentheses