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Fig. 1 | BMC Plant Biology

Fig. 1

From: Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction

Fig. 1

Model Prediction Performance Across Soybean Traits. A Accuracy for flower colour, pod colour, pubescence density and seed coat colour for models trained on SNP input data uniformly distributed across the soybean genome. B Root mean square error as a percentage of mean trait value for seed oil as a percentage of total seed weight, seed protein as a percentage of total seed weight and total seed weight. Models were trained on SNP input data uniformly distributed across the soybean genome. C Accuracy for flower colour, pod colour, pubescence density and seed coat colour for models trained on reduced SNP input data set. D Root mean square error as a percentage of mean trait value for seed oil as a percentage of total seed weight, seed protein as a percentage of total seed weight and total seed weight. Models were trained on a reduced SNP input data set

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BMC Plant Biology

ISSN: 1471-2229

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