My suggestion is to use sklearn.multioutput.MultiOutputRegressor as a wrapper of
MultiOutputRegressor trains one regressor per target and only requires that the regressor implements
predict, which xgboost happens to support.
# get some noised linear data X = np.random.random((1000, 10)) a = np.random.random((10, 3)) y = np.dot(X, a) + np.random.normal(0, 1e-3, (1000, 3)) # fitting multioutputregressor = MultiOutputRegressor(xgb.XGBRegressor(objective="reg:linear")).fit(X, y) # predicting print np.mean((multioutputregressor.predict(X) - y)**2, axis=0) # 0.004, 0.003, 0.005
This is probably the easiest way to regress multi-dimension targets using xgboost as you would not need to change any other part of your code (if you were using the
sklearn API originally).
However this method does not leverage any possible relation between targets. But you can try to design a customized objective function to achieve that.