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Short and sweet answer: The answers above are very thorough, so here is a quick description of how LDA and SVM work. Support vector machines find a linear separator (linear combination, hyperplane) that separates the classes with the least error, and chooses the separator with the maximum margin (the width that the boundary could be increased before ...


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$k$-NN just measures the distances between observations and may suffer the curse of dimensionality as well as other algorithms. It also does not try finding the distribution of the variables, just makes local approximations. So it is hard to compare to the two other methods you mention. Logistic regression (same applies to linear regression) makes the ...


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I see many amazing answers, and I don't know how an humble self made classification may be received, but I don't know any all-comprensive book of all statistics to show the summary of, and I do think that, as @mkt brillantly commented, a classification of a study field can be useful. So, here is my shot: descriptive statistics simple inference simple ...


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I do not quite understand what you mean by “optimistic”. Training on a balanced set and testing on an imbalanced set is fine to me, as long as the test set model the real distribution of the data well and the classifier performs well. However, if you want to estimate the precision on the imbalanced set based on the performance on the training set, that will ...


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