Save python random forest model to file

...import cPicklerf = 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)

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

The example:

import joblibfrom sklearn.ensemble import RandomForestClassifier# create RFrf = RandomForestClassifier()# fit on some datarf.fit(X, y)# savejoblib.dump(rf, "my_random_forest.joblib")# loadloaded_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.

I use dill, it stores all the data and I think possibly module information? Maybe not. I remember trying to use pickle for storing these really complicated objects and it didn't work for me. cPickle probably does the same job as dill but i've never tried cpickle. it looks like it works in literally the exact same way. I use "obj" extension but that's by no means conventional...It just made sense for me since I was storing an object.

import dillwd = "/whatever/you/want/your/working/directory/to/be/"rf= RandomForestRegressor(n_estimators=250, max_features=9,compute_importances=True)rf.fit(Predx, Predy)dill.dump(rf, open(wd + "filename.obj","wb"))

btw, not sure if you use iPython, but sometimes writing a file that way doesn't so you have to do the:

with open(wd + "filename.obj","wb") as f:    dill.dump(rf,f)

call the objects again:

model = dill.load(open(wd + "filename.obj","rb"))