One of the key challenges for any quantitative modeler is selecting the appropriate modeling technique. This involves not selecting the features or predictors to be included in the model, but rather the statistical techniques that will generate the best predictions. Not all modeling approaches are the same, so knowing when to use the best techniques is an important skill. The paper "From OLS to neural nets: Comparing regression models in systematic trading" is not the final word on this question. Still, it does a good job of making comparisons across techniques. It does not address all the machine learning techniques, yet it does compare many of the simple techniques that most researchers may employ.
The tables from the paper are nice to have handy for anyone starting out when thinking about modeling.
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