Save python random forest model to file

Question

In R, after running "random forest" model, I can use save.image("***.RData") to store the model. Afterwards, I can just load the model to do predictions directly.

Can you do a similar thing in python? I separate the Model and Prediction into two files. And in Model file:

rf= RandomForestRegressor(n_estimators=250, max_features=9,compute_importances=True) fit= rf.fit(Predx, Predy) 

I tried to return rf or fit, but still can't load the model in the prediction file.

Can you separate the model and prediction using the sklearn random forest package?

Answer 1

... import cPickle  rf = RandomForestRegresor() rf.fit(X, y)  with open('path/to/file', 'wb') as f:     cPickle.dump(rf, f)   # in your prediction file                                                                                                                                                                                                             with open('path/to/file', 'rb') as f:     rf = cPickle.load(f)   preds = rf.predict(new_X) 

Answer 2

You can use joblib to save and load the Random Forest from scikit-learn (in fact, any model from scikit-learn)

The example:

import joblib from sklearn.ensemble import RandomForestClassifier # create RF rf = RandomForestClassifier() # fit on some data rf.fit(X, y)  # save joblib.dump(rf, "my_random_forest.joblib")  # load loaded_rf = joblib.load("my_random_forest.joblib")  

What is more, the joblib.dump has compress argument, so the model can be compressed. I made very simple test on iris dataset and compress=3 reduces the size of the file about 5.6 times.