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?