Python package to perform logistic regression with continuous target between 0 and 1?

Question

my inputs are feature vectors, and my outputs/targets are floats between 0 and 1 for training.

I am familiar that XGBRFRegressor and XGBRegressor are capable of this, but I am seeking logistic regression equivalent rather than using gradient boosting. I guess I could manually do the log transformations turning it into a standard linear regression problem, but seeking to see if there is already a solution for this.

Thanks, Will

Answer 1

I solved this by transforming to linear scale then training using any regression method, and then transforming back with a log transformation.

see an example at how to use sklearn when target variable is a proportion