Farthest point sampling in property-designated chemical feature space produces more diverse training sets that improve predictive accuracy and reduce overfitting in ML models for small chemical datasets compared with random sampling.
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Farthest Point Sampling in Property Designated Chemical Feature Space as a General Strategy for Enhancing the Machine Learning Model Performance for Small Scale Chemical Dataset
Farthest point sampling in property-designated chemical feature space produces more diverse training sets that improve predictive accuracy and reduce overfitting in ML models for small chemical datasets compared with random sampling.