How to save & load xgboost model? [closed]

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

Both functions save_model and dump_model save the model, the difference is that in dump_model you can save feature name and save tree in text format.

The load_model will work with model from save_model. The model from dump_model can be used for example with xgbfi.

During loading the model, you need to specify the path where your models is saved. In the example bst.load_model("model.bin") model is loaded from file model.bin - it is just a name of file with model. Good luck!

EDIT: From Xgboost documentation (for version 1.3.3), the dump_model() should be used for saving the model for further interpretation. For saving and loading the model the save_model() and load_model() should be used. Please check the docs for more details.

There is also a difference between Learning API and Scikit-Learn API of Xgboost. The latter saves the best_ntree_limit variable which is set during the training with early stopping. You can read details in my article How to save and load Xgboost in Python?

The save_model() method recognize the format of the file name, if *.json is specified, then model is saved in JSON, otherwise it is text file.

An easy way of saving and loading a xgboost model is with joblib library.

import joblib
#save model
joblib.dump(xgb, filename) 

#load saved model
xgb = joblib.load(filename)

Don't use pickle or joblib as that may introduces dependencies on xgboost version. The canonical way to save and restore models is by load_model and save_model.

If you’d like to store or archive your model for long-term storage, use save_model (Python) and xgb.save (R).

This is the relevant documentation for the latest versions of XGBoost. It also explains the difference between dump_model and save_model.

Note that you can serialize/de-serialize your models as json by specifying json as the extension when using bst.save_model. If the speed of saving and restoring the model is not important for you, this is very convenient, as it allows you to do proper version control of the model since it's a simple text file.