Summary

After reading this chapter, you should now know the approaches that are used to win data mining and machine learning competitions. Automated tuning methods can assist with squeezing every bit of performance out of a single model. On the other hand, tremendous gains are possible by creating groups of machine learning models called ensembles, which work together to achieve greater performance than single models can by working alone. A variety of tree-based algorithms, including random forests and gradient boosting, provide the benefits of ensembles but can be trained as easily as a single model. On the other hand, learners can be stacked or blended into ensembles by hand, which allows the approach to be carefully tailored to a learning problem.

With a variety of options for improving the performance of a model, where should someone begin? There is no single best approach, but practitioners tend to fall into one of three camps. First, some begin with one of the more sophisticated...