Adaptive Boosting（AdaBoost）、Gradient-Boosted Decision Trees（GBDT）、Random Forest（RF）、Extreme Gradient Boosting（XGboost）、Light Gradient Boosting Machine（lightGBM）的比较分析

文章目录

• • • • AdaBoost
  • GBDT
  • Random Forest
  • XGboost
  • LightGBM
  • CatBoost
  • 对比分析

AdaBoost

简单介绍

Adaptive Boosting (AdaBoost) is fundamentally rooted in the boosting paradigm. It achieves its goal by combining multiple weak classifiers through a weighted summation process to form a strong classifier. During the training phase, the algorithm pays particular attention to samples that are frequently misclassified, assigning them higher weights. Classifiers with higher accuracy receive proportionally larger weights in subsequent iterations.

*更深一步的介绍