# XGBoost Predictions all the same

When I evaluate the model I seem to be getting a decent RMSE score but when I try to actually see the predictions when I call the model all my values are the same.

 xdata = xgboost.DMatrix(X_train, y_train, feature_names=all_vars)
xdata_val = xgboost.DMatrix(X_valid, y_valid, feature_names=all_vars)
xgb_parms['seed'] = random.randint(0,1e9)
model = xgboost.train(xgb_parms, xdata)
model.eval(xdata_val)
ypred = model.predict(xdata_val)


I believe the error is on my last step, what am I doing wrong?

• Try to draw the XGB tree and see how it looks like. Since XGB is a tree model, it might overfit (if the data is unbalanced for instance) generating a small tree that always returns the frequent class label. – Abdulrahman Bres Mar 28 '18 at 23:36