This chapter explores boosting, an alternative ensemble algorithm for decision trees that often produces even better results. The key difference is that boosting modifies the training data for each new tree based on the cumulative errors made by the model so far. In contrast to random forests that train many trees independently using samples of the

Over the last three decades, boosting has become one of the most successful machine learning (ML) algorithms, dominating many ML competitions for structured, tabular data (as opposed to high-dimensional image or speech data with a more complex input-out relationship where deep learning excels).

participants and anticipate their next move.

Individual random forest trees are usually grown deep to ensure low bias while relying on the randomized training process to produce different, uncorrelated prediction errors that have a lower variance when aggregated than individual tree predictions.