What technique samples the training dataset for each individual tree while allowing data examples to appear in multiple models?

The correct choice is based on the concept of bagging, or bootstrap aggregating, which is a technique specifically designed for creating multiple models through the sampling of the training dataset. In bagging, each tree in the ensemble is trained on a different subset of the training data, which is obtained using random sampling with replacement. This means that the same data points can appear multiple times within different subsets, allowing for a diverse set of training datasets for each individual tree.

This approach helps to reduce variance and improve the accuracy of the model by averaging the predictions from multiple trees, effectively minimizing overfitting. By aggregating the predictions of these individually trained trees, bagging enhances the overall robustness of the model compared to a single decision tree.

While boosting, stacking, and subsampling are related concepts in ensemble methods, they differ from bagging in their processes and objectives. Boosting typically focuses on training models sequentially, where each model tries to correct the errors of its predecessor. Stacking involves combining multiple models to improve predictions by learning how to best combine their outputs. Subsampling generally refers to taking random samples from the dataset without the replacement mechanism applied in bagging, which could limit the models' variance reduction capabilities.