Save python random forest model to file

user_3013706 0
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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?

scikit-learn machine-learning random-forest python
5 Answers

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.

user_56059190

I'd reiterate that joblib does the job well and it provides really good compression options (ie lzma).

with open("clf.pkl", "wb") as out: pickle.dump(clf, out)
with open("clf.dill", "wb") as out: dill.dump(clf, out)
joblib.dump(clf, "clf.jbl")
joblib.dump(clf, "clf.jbl.lzma")
joblib.dump(clf, "clf.jbl.gz")

!du clf.*
24576   clf.dill
24576   clf.jbl
5120    clf.jbl.gz
3072    clf.jbl.lzma
24576   clf.pkl
user_6322420
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