Abstract
Data splitting is an integral step in machine learning that ensures good model generalization. The novel support points-based split method has been evaluated on several datasets (e.g. Iris dataset, etc.) and has shown to be promising than conventional methods (e.g. the random data split). However, this method has never been applied in soil-based research. Therefore, the current study compared soil organic carbon (SOC) RMSE prediction results generated through the conventional random split and the novel support points-based split methods. While applying the above-mentioned methods, data were partitioned into train and test sets based on four percentage ratios of 60/40, 70/30, 75/25 and 80/20. Generally, test RMSE results based on the two split methods as well as percentage ratios were comparable. Nonetheless, the novel method is more reliable and robust since it applies iterations to perform the splitting process while utilizing control points to establish an optimal data partition.
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