Stacking (RapidMiner Studio Core)

Description

Stacked generalization (or stacking) is a way of combining multiple models, that introduces the concept of a meta learner. Unlike bagging and boosting, stacking may be (and normally is) used to combine models of different types. The procedure is as follows: Split the training set into two disjoint sets. Train several base learners on the first part. Test the base learners on the second part. Using the predictions from step 3 as the inputs, and the correct responses as the outputs, train a higher level learner. Note that steps 1 to 3 are the same as cross-validation, but instead of using a winner-takes-all approach, we combine the base learners, possibly nonlinearly.

The crucial prior belief underlying the scientific method is that one can judge among a set of models by comparing them on data that was not used to create any of them. This prior belief is used in the cross-validation technique, to choose among a set of models based on a single data set. This is done by partitioning the data set into a training data set and a testing data set; training the models on the training data; and then choosing whichever of those trained models performs best on the testing data.

Stacking exploits this prior belief further. It does this by using performance on the testing data to combine the models rather than choose among them, thereby typically getting a better performance than any single one of the trained models. It has been successfully used on both supervised learning tasks (e.g. regression) and unsupervised learning (e.g. density estimation).

The Stacking operator is a nested operator. It has two subprocess: the Base Learners and the Stacking Model Learner subprocess. You need to have a basic understanding of subprocesses in order to apply this operator. Please study the documentation of the Subprocess operator for basic understanding of subprocesses.

Ensemble Theory Stacking is an ensemble method, therefore an overview of the Ensemble Theory has been discussed here. Ensemble methods use multiple models to obtain a better predictive performance than could be obtained from any of the constituent models. In other words, an ensemble is a technique for combining many weak learners in an attempt to produce a strong learner. Evaluating the prediction of an ensemble typically requires more computation than evaluating the prediction of a single model, so ensembles may be thought of as a way to compensate for poor learning algorithms by performing a lot of extra computation.

An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built. Thus, ensembles can be shown to have more flexibility in the functions they can represent. This flexibility can, in theory, enable them to over-fit the training data more than a single model would, but in practice, some ensemble techniques (especially bagging) tend to reduce problems related to over-fitting of the training data.

Empirically, ensembles tend to yield better results when there is a significant diversity among the models. Many ensemble methods, therefore, seek to promote diversity among the models they combine. Although perhaps non-intuitive, more random algorithms (like random decision trees) can be used to produce a stronger ensemble than very deliberate algorithms (like entropy-reducing decision trees). Using a variety of strong learning algorithms, however, has been shown to be more effective than using techniques that attempt to dumb-down the models in order to promote diversity.