Here in Part 6: Enhancing Model Performance, we are learning methods that allow us to improve the performance of our models. In Chapter 24 we learned about Segmentation Models, where a useful clustering or subdivision of the data set is found, allowing us to develop cluster-specific models for each segment, and thereby enhancing the overall efficacy of the classification task. Here in this chapter, we are introduced to Ensemble Methods, specifically, bagging and boosting that combine the results from a set of classification models (classifiers), in order to increase the accuracy and reduce the variability of the classification. Next time, in Chapter 26, we consider other types of ensemble methods, including voting and model averaging.
We have become acquainted with a wide range of classification algorithms in this book, including
However, we have so far used our classification algorithms one at a time. Have you wondered what would happen if we were somehow able to combine more than one classification model? Might the resulting combined model be more accurate, or have less variability?
What would be the rationale for using an ensemble of classification models?
The benefits of using ...