Data Science Programming All-in-One For Dummies

Chapter 1

Leveraging Ensembles of Learners

IN THIS CHAPTER

Considering decision trees

Performing predictions

Using gradient descent

Working with multiple predictors

After discovering so many complex and powerful algorithms, you might be surprised to discover that a summation of simpler machine learning algorithms can often outperform the most sophisticated solutions. Such is the power of ensembles, which are groups of models made to work together to produce better predictions. The amazing thing about ensembles is that they are made up of groups of singularly nonperforming algorithms.

Ensembles don’t work much differently from the collective intelligence of crowds, through which a set of wrong answers, if averaged, provides the right answer. Sir Francis Galton, the British, Victorian-era statistician known for having formulated the idea of correlation, narrated the anecdote of a crowd in a county fair that could guess correctly the weight of an ox after all the people’s previous answers were averaged. You can find similar examples everywhere and easily re-create the experiment by ...