Stacking, like bagging and boosting, is an ensemble learning technique. We could only combine weak models that employed the same learning techniques, such as logistic regression, in bagging and boosting. Homogeneous learners are the name given to these models. Stacking, on the other hand, allows us to combine weak models that employ various learning techniques. Heterogeneous learners are those who learn in a variety of ways. Stacking works by training many (and diverse) weak models or learners, then combining them by training a meta-model to create predictions based on the many outputs or predictions produced by the numerous weak models.