AdaBoost

Python 实现: AdaBoost - Donny-Hikari - Github Introduction AdaBoost 是 Adaptive Boosting 的简称。 Boosting 是一种 Ensemble Learning 方法。 其他的 Ensemble Learning 方法还有 Bagging, Stacking 等。 Bagging, Boosting, Stacking 的区别如下： Bagging: Equal weight voting. Trains each model with a random drawn subset of training set. Boosting: Trains each new model instance to emphasize the training instances that previous models mis-classified. Has better accuracy comparing to bagging, but also tends to overfit. Stacking: Trains a learning algorithm to combine the predictions of several other learning algorithms. The Formulas Given a N*M matrix X, and a N vector y, where N is the count of samples, and M is the features of samples. AdaBoost trains T weak classifiers with the following steps: 给定一个N*M的矩阵X（特征），和一个N维向量y（标签），N为样本数，M为特征维度。AdaBoost以一下步骤训练T个弱分类器：