Abstract
Regional accuracy was examined using extreme gradient boosting (XGBoost) to improve frost prediction accuracy, and accuracy differences by region were found. When the points were divided into two groups with weather variables, Group 1 had a coastal climate with a high minimum temperature, humidity, and wind speed and Group 2 exhibited relatively inland climate characteristics. We calculated the accuracy in the two groups and found that the precision and recall scores in coastal areas (Group 1) were significantly lower than those in the inland areas (Group 2). Geographic elements (distance from the nearest coast and height) were added as variables to improve accuracy. In addition, considering the continuity of frost occurrence, the method of reflecting the frost occurrence of the previous day as a variable and the synthetic minority oversampling technique (SMOTE) pretreatment were used to increase the learning ability.
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