Application of Gradient Boosting Algorithm in Hydraulic Support Selection of Typical Shallow Coal Seam

A new method using gradient boosting regression tree(GBRT) which is optimized by logistic regression(LR) to predict working resistance of hydraulic support is proposed, avoiding shortcomings of current determining methods. Add learning rate to GBRT to limit the learning rate of sub-models and prevent over-fitting; using LR to optimize sample parameters to establish LR-GBRT regression prediction model; the model is applied to predict resistance of hydraulic support, and the prediction result is compared with linear regression(LR), support vector model(SVM), decision tree regression(DTR), elastic net regression(EN). The results show that the model has better generalization performance and higher prediction accuracy. It can effectively predict resistance of hydraulic support