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> I’ve grown fond of just throwing a light gbm model at everything and calling it a day It is not always a good idea to do that. Always try different methods,
by teruakohatu 5y ago
> I’ve grown fond of just throwing a light gbm model at everything and calling it a day
It is not always a good idea to do that. Always try different methods, there is no ultimate method. At the very least OLS should be tried and some other fully explainable methods, even a simple CART like method.
- csee 5y agoOLS is my default go to. It outperforms a random forest so often in small data real world applications over nonstationary data, and model explainability is built into it. If I'm working in a domain with stationary data then I'd tilt more to the forest (due to not having to engineer features, and the inbuilt ability to detect non-linear relationships and interactions between features).
- deleted 5y ago[deleted]