Need to append bias term when using `sklearn` models?
No, you do not add any biases, models define biases in their own way. What you learned during course is generic, although not perfect - solution. It matters for models such as SVM, which should not ever have appended "1"s, as then this bias would get regularized, which is simply wrong for SVMs. Thus, while this is nice theoretical trick to show that you can actually create methods completely ignoring bias, in practise - it is often treated in a specific way, and scikit-learn does it for you.
In my machine learning class, we have learned about appending a 1 to each sample's feature vector when using many machine learning models to account for bias. For example, if we are doing linear regression and a sample has features f_1, f_2, ..., f_d, we need to add a "fake" feature value of 1 to allow for the regression function to not have to pass through the origin.
When using sklearn models, do you need to do this yourself, or do their implementations do it for you? Specifically, I'm interested in whether or not this is necessary when using any of their regression models or their SVM models.