An introduction to imbalanced classification using Ensemble techniques and deployed on heroku.
Resources at bottom👇🏻
Intents define an event in virtual assistant systems such as voice assistants, chatbots, etc.
The dataset used available here,
includes collection of json files consisting of train, val and test data. Training data has 2 columns:
- command from user
- corrsponding intent
In total there are 151 intents including a special intent aka oos(out-of-scope) intent, it is difficult for chatbots to differentiate between an in scope intent from an out of scope intent.
Model has been deployed using Flask on Heroku SaaS
Visit to see live demo! --> https://intent-classification-python.herokuapp.com/
First highlighted block in output specifies the guessed intent whereas the second block specifies whether the intent is with scope of bot or not i.e is it an inscope or outscope intent which might not always be accourate provided the training data, view IPy notebook for analysis.
- https://machinelearningmastery.com/what-is-imbalanced-classification/
- https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/
- https://www.analyticsvidhya.com/blog/2017/03/imbalanced-data-classification/
- https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.ensemble.EasyEnsembleClassifier.html
- https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.ensemble.RUSBoostClassifier.html