Tuning the hyperparameter with gridsearch results in overfitting. The train error is definitely low, but the test error is high. Can’t you adjust the hyperparameter to lower the test error? before tuning train_error: 0.386055, test_error: 0.674069 -after tuning train_error: 0.070645, test_error: 0.708254 Answer It all depends on the data you are training. If the data you are using for training
Tag: xgboost
GridSearchCV progress in Jupiter Notebook
Is it possible to see the progress of GridSearchCV in a Jupyter Notebook? I’m running this script in python: I can see only some warnings in the output of the cell. Answer You want the verbose parameter: An example of what I got on toy data:
What does the value of ‘leaf’ in the following xgboost model tree diagram means?
I am guessing that it is conditional probability given that the above (tree branch) condition exists. However, I am not clear on it. If you want to read more about the data used or how do we get this diagram then go to : http://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ Answer Attribute leaf is the predicted value. In other words, if the evaluation of a