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1/2 Lin reg is ML but is interpretable and not a black box. Are there other ML models that we can intuitively understand, rather than view as blackbox? Trying to avoid blackbox as much as possible, but much discussion of SVMs for ex treats them like this we can visualze
All kinds of regression models are easy to interpret (look up generalised linear model) as are decision trees (even random forests to some extent) and nearest neighbour.
You can also get a lot of mileage from building your own features which are nonlinear transformations of your base features, and running linear regression on them.
This, in spades.
fairly popular tool now for forecasting tradeable stuff, tho the efficacy is debatable, esp when some rsrch teams dont split data into train and test period, dont verify model assumptions, etc., sometimes gets used for deceptive persuasion to generate churn
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