Incremental training of random forest model using python sklearn

Question

I am using the below code to save a random forest model. I am using cPickle to save the trained model. As I see new data, can I train the model incrementally. Currently, the train set has about 2 years data. Is there a way to train on another 2 years and (kind of) append it to the existing saved model.

rf =  RandomForestRegressor(n_estimators=100)
print ("Trying to fit the Random Forest model --> ")
if os.path.exists('rf.pkl'):
    print ("Trained model already pickled -- >")
    with open('rf.pkl', 'rb') as f:
        rf = cPickle.load(f)
else:
    df_x_train = x_train[col_feature]
    rf.fit(df_x_train,y_train)
    print ("Training for the model done ")
    with open('rf.pkl', 'wb') as f:
        cPickle.dump(rf, f)
df_x_test = x_test[col_feature]
pred = rf.predict(df_x_test)

EDIT 1: I don't have the compute capacity to train the model on 4 years of data all at once.

Answer 1

You can set the 'warm_start' parameter to True in the model. This will ensure the retention of learning with previous learn using fit call.

Same model learning incrementally two times (train_X[:1], train_X[1:2]) after setting ' warm_start '

forest_model = RandomForestRegressor(warm_start=True)
forest_model.fit(train_X[:1],train_y[:1])
pred_y = forest_model.predict(val_X[:1])
mae = mean_absolute_error(pred_y,val_y[:1])
print("mae      :",mae)
print('pred_y :',pred_y)
forest_model.fit(train_X[1:2],train_y[1:2])
pred_y = forest_model.predict(val_X[1:2])
mae = mean_absolute_error(pred_y,val_y[1:2])
print("mae      :",mae)
print('pred_y :',pred_y)

mae : 1290000.0 pred_y : [ 1630000.] mae : 925000.0 pred_y : [ 1630000.]

Model only with the last learnt values ( train_X[1:2] )

forest_model = RandomForestRegressor()
forest_model.fit(train_X[1:2],train_y[1:2])
pred_y = forest_model.predict(val_X[1:2])
mae = mean_absolute_error(pred_y,val_y[1:2])
print("mae      :",mae)
print('pred_y :',pred_y)

mae : 515000.0 pred_y : [ 1220000.]

http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html