Comparison of Ensemble Machine Learning Methods for Predicting the End of the Frost Period

Abstract

we studied the performance of ensemble machine learning methods for predicting the end of the frost period. We provided a brief overview of the considered ensemble approaches and investigated how different hyperparameter settings and input data configurations affect model training. We applied several tools, including gradient boosting methods (XGBoost, LightGBM, and CatBoost), random forest (scikit-learn), and logistic regression (scikit-learn), compared the resulting models, and assessed their predictive quality. The results show differences in performance across methods and highlight the impact of hyperparameter tuning and input data selection on prediction accuracy.