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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