I am trying the following:
model = xgb.XGBClassifier( learning_rate = 0.3, n_estimators = 800, max_depth = 8, eval_metric='rmse', #eta = 0.1, colsample_bytree= 1, objective= 'reg:squarederror', subsample= 1.0, min_child_weight= 4, num_class = 6)
I would like to indeed verify if this works. That is it will optimize the square error between the classes.
When I call the fit function and reprint the parametrs of the object
model i see it it reset to
multi:softprob. Is there any way I cam make this work?