Hands-On Machine Learning with C++

Summary

In this chapter, we examined various methods for constructing ensembles of machine learning algorithms. The main purposes of creating ensembles are these:

• Reducing the error of the elementary algorithms
• Expanding the set of possible hypotheses
• Increasing a probability of reaching the global optimum during optimizing

We saw that there are three main approaches for building ensembles: training elementary algorithms on various datasets and averaging the errors (bagging); consistently improving the results of the previous, weaker algorithms (boosting); and learning the meta-algorithm from the results of elementary algorithms (stacking). Note that the methods of building ensembles that we've covered, except stacking, require that the ...