What is model stacking in neural networks in R

This recipe explains what is model stacking in neural networks

Recipe Objective - What is model stacking in neural networks?

Model stacking is a technique of model ensembling and is defined as a meta-algorithm that aims at combining different neural network models to get a better prediction. Model stacking involves training of base-level models on the complete training set, then the meta-model is trained on features that are outputs of a base-level model. The base-level in model stacking often consists of the different learning algorithms and therefore stacking ensembles are often heterogeneous in nature. In Model stacking, models that is Base-Model are typically different (for eg. not all decision trees) and fits on same dataset and also, the single model that is Meta-model is used to learn how to best combine predictions from contributing models.

This recipe explains what is Model Stacking, how it is beneficial for neural network models and how it can be executed.

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Explanation of Model Stacking.

Model stacking mostly considers heterogeneous weak learners, learns them in the parallel and combines them by training the meta-model to output the prediction based on the different weak models predictions. Model stacking mostly try to produce strong models which are less biased than their components that is even if variance can be reduced.

To build a stacking model, the L learners that are to be fit and the meta-model that combines them, both to be defined in order to build a strong stacked model.

Building a stack model with assumingly composed of L weak learners, firstly split the training data in two folds or k-fold cross-validation can also be used. Then, choose L weak learners and fit them to the data of the first fold. Furthermore, for each of the L weak learners, predictions are made for observations in the second fold. Then, fit the meta-model on the second fold and finally using the predictions made by weak learners as the inputs.

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