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When dealing with binary classification, I most often find myself estimating a logistic regression model. I have tried a few other approaches as well, but to be honest I feel like I know way too little about all of the other possibilities.

I understand that it isn't as easy as following a 'model choice' guide. So what I'm looking for is a source where I can read up on the theory behind the different approaches, and then take it from there. Any ideas?

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An Introduction to Statistical Learning also covers linear and quadratic discriminant analysis, tree-based methods (classification trees, random forests, bagging, and boosing), and support vector machines at a reasonably introductory level. The Elements of Statistical Learning goes into more depth, and adds in neural networks.

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