

Common mistakes when building machine learning models - chengtao
http://ml.posthaven.com/machine-learning-done-wrong

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kiyoto
A great post!

>When building a binary classifier, many practitioners immediately jump to
logistic regression because it’s simple. But, many also forget that logistic
regression is a linear model and the non-linear interaction among predictors
need to be encoded manually. Returning to fraud detection, high order
interaction features like "billing address = shipping address and transaction
amount < $50" are required for good model performance. So one should prefer
non-linear models like SVM with kernel or tree based classifiers that bake in
higher-order interaction features.

I am not seeing the argument here. Can we just not encode binary conditions
with dummy variables?

