Combining logistic and continuous regression with scikit-learn
If your target data Y has multiple columns you need to use multi-task learning approach. Scikit-learn contains some multi-task learning algorithms for regression like multi-task elastic-net but you cannot combine logistic regression with linear regression because these algorithms use different loss functions to optimize. Also, you may try neural networks for your problem.
In my dataset X I have two continuous variables a, b and two boolean variables c, d, making a total of 4 columns. 
I have a multidimensional target y consisting of two continuous variables A, B and one boolean variable C. 
I would like to train a model on the columns of X to predict the columns of y. However, having tried LinearRegression on X it didn't perform so well (my variables vary several orders of magnitude and I have to apply suitable transforms to get the logarithms, I won't go into too much detail here). 
I think I need to use LogisticRegression on the boolean columns. 
What I'd really like to do is combine both LinearRegression on the continuous variables and LogisticRegression on the boolean variables into a single pipeline. Note that all the columns of y depend on all the columns of X, so I can't simply train the continuous and boolean variables independently.
Is this even possible, and if so how do I do it?