Multi target classification for different types of target variables
You have one classification task and one regression task, but sklearn's multioutput meta-estimators only support two tasks of the same type.

The best solution here is to train two models: 


A binary classifier to predict $target1$ 
A regressor to predict $target2$


For example:

from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

# X, y = load training data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

clf = RandomForestClassifier()
clf.fit(X_train, y_train[:,0])
print("classifier accuracy:", clf.score(X_test, y_test[:,0]))

regr = RandomForestRegressor()
regr.fit(X_train, y_train[:,1)
print("regressor R^2:", regr.score(X_test, y_test[:,1]))