  As a common method in Machine Learning, Ensemble Method is used to train
multiple models from a data set and obtain better results through certain
combination strategies. Stacking method, as representatives of Ensemble
Learning methods, is often used in Machine Learning Competitions such as
Kaggle. This paper proposes a variant of Stacking Model based on the idea of
gcForest, namely Adaptive Generation Model (AGM). It means that the adaptive
generation is performed not only in the horizontal direction to expand the
width of each layer model, but also in the vertical direction to expand the
depth of the model. For base models of AGM, they all come from preset basic
Machine Learning Models. In addition, a feature augmentation method is added
between layers to further improve the overall accuracy of the model. Finally,
through comparative experiments on 7 data sets, the results show that the
accuracy of AGM are better than its previous models.
