How to use stacking of Machine Learning Algorithms in R

Stacking is a technique that can be used to improve the performance of machine learning algorithms. It is a method of combining the predictions of multiple models to produce a more accurate final prediction. In this blog post, we will discuss how to use stacking of machine learning algorithms in R.

The first step in using stacking is to train several base models on the same data. These base models can be different algorithms or different configurations of the same algorithm. For example, you could train a Random Forest, a Support Vector Machine, and a Neural Network on the same data.

Once the base models have been trained, the next step is to use these models to make predictions on a new dataset. These predictions can then be used as input features for a new model, called the meta-model. The meta-model is trained to predict the final outcome based on the predictions of the base models.

The key advantage of stacking is that it allows the base models to focus on what they do best, while the meta-model is able to learn how to combine the predictions of the base models to make a more accurate final prediction. This can lead to improved performance compared to using a single model alone.

In R, the package ‘caret’ provides a function caretEnsemble() which can be used to stack different models. This function takes the base models and the meta-model as input, and can be used to train and make predictions using the stacked models.

In summary, stacking is a powerful technique that can be used to improve the performance of machine learning algorithms. By training multiple base models on the same data and using these predictions as input for a meta-model, you can create a more accurate final prediction. The package ‘caret’ in R provides an easy-to-use function for stacking different models.

 

In this Applied Machine Learning Recipe, you will learn: How to use stacking of Machine Learning Algorithms in R.

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