Improving random forest algorithm by Lasso method
Hui Wang, Guizhi Wang · Journal of Statistical Computation and Simulation · 2020
The random forest (RF) algorithm is a very practical and excellent ensemble learning algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called ‘post-selection boosting random forest’ (PBRF). This algorithm combines the original random forest and the Lasso method, without giving the number of decision trees for final prediction in advance, it can dynamically obtain the decision trees according to different input samples to output the prediction results. Meanwhile, we verify that the proposed algorithm can improve the performance of the model through simulation studies and real data analysis.