Re: [scikit-learn] How is linear regression in scikit-learn done? Do you need train and test split?

Hi CW, It's not about the concept of the black box, none of the algorithms in sklearn are a blackbox. The question is about model validity. Is linear regression a valid representation of your data? That's what the train/test answers. You may think so, but only this process will answer it properly.

Re: [scikit-learn] How is linear regression in scikit-learn done? Do you need train and test split?

Dear CW, > Linear regression is not a black-box. I view prediction accuracy as an > overkill on interpretable models. Especially when you can use R-squared, > coefficient significance, etc. > Following on my previous note about being cautious with cross-validated evaluation for classification, t

Re: [scikit-learn] How is linear regression in scikit-learn done? Do you need train and test split?

Thank you all for the replies. I agree that prediction accuracy is great for evaluating black-box ML models. Especially advanced models like neural networks, or not-so-black models like LASSO, because they are NP-hard to solve. Linear regression is not a black-box. I view prediction accuracy as a