Skip to content

Train microclassifiers in the cloud for spam detection, sentiment analysis and more.

Notifications You must be signed in to change notification settings

lambdacasserole/classr

Repository files navigation

Classr

Train microclassifiers in the cloud for spam detection, sentiment analysis and more.

Classr logo

Overview

Classr is a web app that allows users to create microclassifiers. A microclassifier is just a machine learning model (classifier) that is trained using a minimal (but still reasonable) amount of training data (4MB max).

Screenshot of app

Classr uses the bayes package at its core with custom additions for calculation of precision/recall/F1 score etc. and generation and rendering of confusion matrices.

Why this project?

I wanted to see what I couuld do working on something from scratch with the T3 stack while sharpening my frontend skills in the process.

Turns out, it's a super cool stack that you should definitely try out if you haven't already!

Limitations

Classr trains multinomial naive bayes classifiers behind the scenes on unigrams generated by splitting strings along space characters. Train/test split is 20/80 and Laplace smoothing is at 1.

Any ML engineer with tell you that this is a very simple classifier with a lot of caveats. Use appropriately, and wield responsibly.

Classr does not support any of the following features (yet!):

  • Training on n-grams.
  • Tokenization based on anything but splitting documents along spaces.
  • Stopword removal.
  • More advanced classifiers (SVMs, random forests etc.)

Deployment

This app is designed for deployment on Vercel but you might also be able to get it to work with Docker (I haven't tried this myself yet).