Risk Classification Model of Human-Tiger Coexistence Area Based on XGBoost Algorithm
Based on the data of Siberian tigers taken by infrared cameras in Hunchun from 2014 to 2019,a risk classification model of tiger infested areas was constructed using XGBoost algorithm.The model test showed that the accuracy rate of the model was 93.51%,the precision was 93.85%,the recall rate was 93.08%,the F1-score value was 93.31%,and the Cohen's Kappa statistical coefficient was 90.2%.The research results showed that the risk classification model of human-tiger coexistence area based on XGBoost algorithm had good classification effect and high prediction accuracy.It was feasible to use this model to classify the risk level of human-tiger coexistence area.
human-tiger coexistence areaXGBoost algorithmrisk levelmodel divisioninfrared camera traps