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...Ensemble methods this is the central illusion in life that randomness a risk it learning objectives bad thing nassimnicholas taleb implement bagging and explain how reduces variance predictor difference between weak learner strong derive adaboost algorithm understand relationship boosting decision stumps linear classication groups of people can often make better decisions than individuals especially when group members each come with their own biases same true machine are models achieve performance by combining opinions multiple learners doing so you get away using much simpler still great moreover ensembles inherantly parallel which them muchmoreefcient at training test time if have access to processors chapter will learn about various ways base into one shocking results we see take model only ever does slightly dependencies chance turn an arbitrarily good though technique known as also howensembles decrease predictors well perform regularization voting classiers all algorithms seen fa...