predict external dataset with models from random forest
First of all, you should not save the result of cross validation. Cross validation is not a training method, it is an evaluation scheme. You should build a single model on your whole dataset and use it to predict.
If, for some reason, you can no longer train your model, you can still use this 5 predictions by averaging them (as random forest itself is a simple averagin ensemble of trees), however going back and retraining should give you bettter results.
I used joblib.dump in python to save models from 5 fold cross validation modelling using random forest. As a result I have 5 models for each dataset saved as: MDL_1.pkl, MDL_2.pkl, MDL_3.pkl, MDL_4.pkl, MDL_5.pkl. Now I want to use these models for prediction of external dataset using predict_proba when the final prediction for each line in my external dataset is an average of 5 models. What is the best way to proceed?
Thank you for your help