OK, here is my honest take on when to use which approach/technique with a given dataset. These are my rules of thumb, and caveats could fill out the entire internet.
1. Up to a few hundred datapoint, use stats
2. For few hundred to few thousand use linear/logistic regression 1/
1. Up to a few hundred datapoint, use stats
2. For few hundred to few thousand use linear/logistic regression 1/
3. Between few thousand to about 10,000 it's anyone's guess. Gradient boosters generally do well here, with other "classical" algorithms.(SVM for instance) sometimes shining. 2/
4. Many thousands to about a billion datapoint is where Gradient Boosted trees rule. If you need just one algorithm, go with this. You'll never go wrong. 3/
5. If you have several billion datapoint, or many times that amount, check out neural nets. They have the capacity to absorb those kinds of datasets easily. 4/
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