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Coding All-in-One For Dummies by Nikhil Abraham

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Chapter 4

Resorting to Ensembles of Learners

IN THIS CHAPTER

check Discovering why many guesses are better than one

check Making uncorrelated trees work well together in Random Forests

check Learning to map complex target functions piece by piece using boosting

check Getting better predictions by averaging models

“Prediction is very difficult, especially if it's about the future.”

— NILS BOHR

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, 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 English Victorian age statistician known for having formulated the idea of correlation, narrated the anecdote of a crowd in ...

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