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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?

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  • $\begingroup$ 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. $\endgroup$ – Abdulrahman Bres Mar 28 '18 at 23:36
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Make sure to pass the model an "objective" parameter and also use "rmse" for the "eval_metric" parameter.

https://github.com/dmlc/xgboost/blob/master/doc/parameter.md

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