There is an interesting angle to this -- I think it maps to the difference between (traditional) statistics and data science.
In traditional stats you are used to small, parsimonious models. In these small models each coefficient, each part of the model is separable in a way, it is meaningful and interpretable by itself. The big thing to avoid is overfitting.
In data science (and/or ML) a lot of models are of the sprawling black-box kind where coefficients are not separable and make no sense outside of the context of the whole model. These models aren't traditionally parsimonious either. Also, because many usual metrics scale badly to large datasets, overfitting has to be managed differently.
In traditional stats you are used to small, parsimonious models. In these small models each coefficient, each part of the model is separable in a way, it is meaningful and interpretable by itself. The big thing to avoid is overfitting.In traditional stats you are used to small, parsimonious models. In these small models each coefficient, each part of the model is separable in a way, it is meaningful and interpretable by itself. The big thing to avoid is overfitting.
Keep in mind that traditional stats also includes semi-parametric and non-parametric meth...
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