Before getting to this part, the dataset preprocessing has to be done and the /backend/dataset/ directory should contain test and train .npy files.
Incase you're not done with that, refer here for preprocessing
The RandomForestClassifier (ensemble learner) is fitted with the training set and then the accuracy and cross validation scores are printed.
The parameters of the learned model, such as number of estimators, tree parameters such as thresholds for each estimators are dumped on to a file named classifier.json.
Change working directory to /backend/classifier and Run
python3 training.py
classifier.py is created in the /static directory.
Serve this classifier.py over HTTP and update URL in the plugin settings.
The same file is also hosted here.