Titlebook: Applied Statistical Learning; With Case Studies in Matthias Schonlau Textbook 2023 The Editor(s) (if applicable) and The Author(s), under e

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The Naive Bayes Classifier,sifier the designation “naive.” The assumption greatly simplifies calculations; the naive Bayes classifier is very fast. The assumption trades off increased bias with reduced variance making the classifier surprisingly successful. The Naive Bayes classifier often benefits from smoothing. We discuss

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Random Forests,ition, at each split, random forests only consider a random subset of x-variables. This promotes the use of a larger number of x-variables and makes the algorithm less dependent on a small number of variables. For any one tree, roughly one third of the observations are not in the bootstrap sample an

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Boosting,g. We talk about variable influence as a way of computing the contribution of individual variables and contrast this approach with variable importance as used in random forests. We discuss tuning parameters and the effect of individual tuning parameters on computing time. We also introduce an increa

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Support Vector Machines, line and the nearest observation of either class is maximized. Often the classes are not separable, i.e., they do not form separate clouds in x-space. In that case, a cost parameter allows for a certain amount of classification error. By deriving additional x-variables (e.g., quadratic terms), we c

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Neural Networks,or regression and multi-class classification. We discuss a number of common activation functions that contribute nonlinearity in an otherwise linear network. We cover vanishing and exploding gradients, weight initialization—to attenuate the vanishing gradient problem—stochastic gradient descent usin

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