Try saving your model after you train on the first batch. Then, on successive runs, provide the xgb.train method with the filepath of the saved model. Here’s a small experiment that I ran to convince myself that it works: First, split the boston dataset into training and testing sets. Then split the training set into … Read more
Please look at this answer here xgboost.train will ignore parameter n_estimators, while xgboost.XGBRegressor accepts. In xgboost.train, boosting iterations (i.e. n_estimators) is controlled by num_boost_round(default: 10) It suggests to remove n_estimators from params supplied to xgb.train and replace it with num_boost_round. So change your params like this: params = {‘objective’: ‘reg:linear’, ‘max_depth’: 2, ‘learning_rate’: .1, ‘min_child_weight’: … Read more
Here is how I solved the problem: import pickle file_name = “xgb_reg.pkl” # save pickle.dump(xgb_model, open(file_name, “wb”)) # load xgb_model_loaded = pickle.load(open(file_name, “rb”)) # test ind = 1 test = X_val[ind] xgb_model_loaded.predict(test)[0] == xgb_model.predict(test)[0] Out[1]: True
The easiest way (Worked for me) is to do the following: anaconda search -t conda xgboost You will get a list of install-able features like this: for example if you want to install the first one on the list mndrake/xgboost (FOR WINDOWS-64bits): conda install -c mndrake xgboost If you’re in a Unix system you can … Read more
Exit code 137 means that your process was killed by (signal 9) SIGKILL . In the case you manually stopped it – there’s your answer. If you didn’t manually stop the script and still got this error code, then the script was killed by your OS. In most of the cases, it is caused by … Read more
In case anyone’s looking for a simpler solution that doesn’t require compiling it yourself: download xgboost whl file from here (make sure to match your python version and system architecture, e.g. “xgboost-0.6-cp35-cp35m-win_amd64.whl” for python 3.5 on 64-bit machine) open command prompt cd to your Downloads folder (or wherever you saved the whl file) pip install … Read more
I used to face the imbalanced dataset before. I used the resample function from sklearn, You can read more from here. The function will randomly copy from the minority to upsampling. In my experience, this method only slightly improve the model, I think you should find data on the internet to add into your dataset.