Multi target classification for different types of target variables I am new to machine learning and I got this task in my university. I have a dataset with over 100 columns and two target variables: $target1$ is categorical i.e. $0$ or $1$ and $target2$ is continuous i.e. values in range $0 \space to \space 100$.

How can I predict this type of problem?

I tried using multi-output classification from sklearn using the Random forest as an ensembler and it is predicting nicely for continuous target variable but not for categorical target variable.
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]))