>I find it amazing that Linear Regression in scikit-learn doesn't return standard errors/t-scores for the coefficients. It's less surprising when you see how s

>I find it amazing that Linear Regression in scikit-learn doesn't return standard errors/t-scores for the coefficients. It's less surprising when you see how some people work with these types of models in Python. They minimize the prediction errors, and if another method has lower error than Linear Regression they use that. There's very limited thinking around diagnosing why a model is not working or modeling at all. As nerdponx pointed out, this is completely opposite to how social scientists or statisticians do modeling work for research. The goal is to support or provide evidence against a hypothesis that has a theoretical basis. You cannot do that by just focusing on minimizing prediction error. Linear Regression in R vs Python is great example of how simple functionality like print(lm) and plot(lm) exposes the different ways of thinking about how to approach a problem. R's lm() provides strong diagnostic functionality out of the box with a few commands, which support people who are focused on diagnosing a model and making adjustments to how the model is defined, not just doing basic transforms (e.g. log) and adjusting hyperparamters to minimize prediction error.