Think Build Implement Repeat
London, UK +44 7367 067226
WhatsApp FOLLOW f in X
Python & Django

Sorting Things Automatically and Correctly

Last updated:

Most classification problems are category problems

Before any model, look at the categories. If your business uses sixty overlapping categories where a person cannot reliably choose between them, no model will either.

Fifteen clear, mutually exclusive categories produce far better classification than sixty overlapping ones, and they produce better reporting too.

Fix the categories first

  1. Take two hundred real items and have two people categorise them independently
  2. Measure how often they agree
  3. Where they disagree, the categories are ambiguous
  4. Merge, split or define more precisely until agreement is high
  5. Then, and only then, build the classifier

Confidence and routing

  • High confidence: route automatically
  • Medium: route with a flag for review
  • Low: send to a person to categorise
  • Corrections captured and added to the evaluation set

Thresholds set from a shadow run against real data, not from intuition. Running in shadow for two weeks gives you the numbers to set them properly.

Monitor the distribution

SignalMeans
Distribution shiftingInput has changed
One category growing sharplyEither real, or a classifier problem
Low-confidence proportion risingInput drifting from the examples
Correction rate risingQuality degrading
A category never usedIt probably should not exist

Where it pays

Support ticket routing, document type identification, enquiry triage, expense categorisation, product classification. All high volume, all currently done by a person reading and deciding.

The value is not just the time. It is the consistency, which makes the resulting data usable for reporting in a way manual categorisation never is.

Frequently asked questions

Do we need to train a model?

Usually not. Modern language models classify well from a description of the categories and a handful of examples.

How accurate is it?

Ninety to ninety-six per cent on well-defined categories. Lower where categories overlap, which is a category problem rather than a model one.

What accuracy do we need?

Better than the current manual consistency, which is frequently lower than people assume. Measure that first.

Should low-confidence items be guessed?

No — route them to a person. A confident wrong classification is worse than an acknowledged uncertain one.

Keep reading

Someone reading and sorting all day?

Check your category agreement first — two people, two hundred items. Then we can talk about automating it.

Book a free 30-minute call Get a project estimate WhatsApp us

Related services

What we build for problems like this one

Custom Software DevelopmentWeb DevelopmentMachine Learning