From the course: Applied Machine Learning: Ensemble Learning
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Concept of stacking
From the course: Applied Machine Learning: Ensemble Learning
Concept of stacking
- [Instructor] In this section, we're going to be exploring stacking. Stacking is a powerful ensemble technique. If you've wondered how to combine the strengths of multiple models to improve predictions, stacking is an answer to that. By the end of this section, you'll understand how stacking works, how to implement it and evaluate it. So stacking is known as stacked generalization, and it is an ensemble technique that combines multiple models. After we have multiple models, a metamodel will use the predictions of these models to make a better prediction. More specifically, we will train these base models, and these base models are trained independently on the data, and they don't have to be the same model type. For example, you could use a decision tree, linear models, neural networks, nearest neighbors, any of the models found in scikit-learn, or models that are compatible with the scikit-learn API. We're going to train each of those models individually. Each of those will make a…
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