muti output regression in xgboost

问题:

Is it possible to train a model in Xgboost that have multiple continuous outputs (multi regression)? What would be the objective to train such a model?

Thanks in advance for any suggestions

回答1:

My suggestion is to use sklearn.multioutput.MultiOutputRegressor as a wrapper of xgb.XGBRegressor. MultiOutputRegressor trains one regressor per target and only requires that the regressor implements fit and 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.