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For those of you that made a transition or embraced machine learning: I'm used to coefficients, coefficients, coefficients from econometric methods. What methods do you use for coefficients in machine learning besides elastic net?
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I feel that the coefficients for each of the covariates make interpretation of algorithm output much clearer since you can clearly see how much impact that particular variable has on the dependent variable (skipping bias, consistency, etc. chain of thoughts). I'm wondering what algorithms you use from machine learning to derive coefficients that's not OLS or elastic net.
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