End-to-End Machine Learning: stacking in R

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End-to-End Machine Learning: stacking in R

Stacking is a technique used in machine learning to improve the performance of a model by combining the predictions of multiple models. It works by training multiple models on the same dataset, then using their predictions as input to a new model called meta-model, which makes the final prediction. The idea behind stacking is that different models may have different strengths and weaknesses, and by combining their predictions through a meta-model, we can improve the overall performance of the model.

In R, there are several libraries such as caretEnsemble, mlrEnsemble and SuperLearner that provide functions for stacking. The process of stacking typically involves the following steps:

 

  1. Divide the dataset into two or more partitions, usually a training set, a testing set, and a validation set.
  2. Train different models on the training set and use them to make predictions on the validation set.
  3. Combine the predictions of the models, by using them as input to a new model, the meta-model, which is trained to make the final prediction.
  4. Use the meta-model to make predictions on the testing set.

 

Stacking can be useful in improving the performance of a model because it allows to combine the strengths of multiple models and to reduce the variance of the model’s predictions. Stacking is especially useful when the dataset is complex and has many features, and when different models might perform better on different subsets of the data.

It’s important to note that stacking can be computationally expensive, especially when the dataset is large or when the number of models to be stacked is large. Additionally, it’s important to use cross-validation to ensure that the stacking improves the performance of the model and that it generalizes well to new data.

Overall, stacking is a powerful technique in R for improving the performance of a machine learning model by combining the predictions of multiple models through a meta-model. It allows to combine the strengths of multiple models and to reduce the variance of the model’s predictions. However, it can be computationally expensive, and it’s important to use cross-validation to ensure that the stacking improves the performance of the model and generalizes well to new data.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: End-to-End Machine Learning: stacking in R.



End-to-End Machine Learning: stacking in R

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