Ensemble Learning: Part 2
Explore ensemble learning techniques including bagging with random forests and sequential boosting. Understand how these methods combine multiple models to enhance prediction accuracy and prevent overfitting. This lesson covers types of bagging, random forests, and popular boosting algorithms to help you apply ensemble approaches in machine learning projects.
We'll cover the following...
Bagging or bootstrap aggregation
Bagging involves building several models using a subset of the original dataset, and then aggregating their individual predictions to make a final prediction. These methods involve introducing randomization during model construction, like decision trees, to reduce the model variance (overfitting). Bagging methods work well with complex models, such as decision trees that have a large depth. Bagging methods differ in the way that they draw subsets from the original dataset and are given the following names.
Types of bagging
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Pasting: When we draw the random subsets of the dataset as the random subset of the samples.
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Bootstrapping: When we draw the samples with replacement. Drawing the samples with replacement means ...