Hello! I want to continue the training of a model with parameter xgb_model assigned in the fit function as follows:
xgb_model_full_data = xgboost_model.fit(train_features, train_labels, eval_metric=custimized_error, eval_set=[(validation_features, validation_labels)], xgb_model=“test_xgb.bin”, early_stopping_rounds=1000)
The training process runs smoothly and the debug information show the final score assume 2.8 with early stopping.
When the fitting is finished, I do the following:
predictions = xgb_model_full_data.predict(validation_features)
The I calculate the score using the predictions and validation_labels, I got 3.23.
I do not understand why the scores are different since the predication is done on the same validation set.
I tried to train the regressor without initializing the xgb_model parameter, and repeat above procedure. The two scores show exactly same value.
What am I missing here?
Thanks and Happy new year.