GPT: Hype, reality, and the Indico generative AI origins story
Read Now
  Everest Group IDP
             PEAK Matrix® 2022  
Indico Named as Major Contender and Star Performer in Everest Group's PEAK Matrix® for Intelligent Document Processing (IDP)
Access the Report


When measuring the classification accuracy on the training set, can I take a sample of it or should I use it entirely?

July 26, 2017 | Ask Slater

Back to Blog

 Usually inference is the fast part so you measure on the whole dataset once per epoch. The problem with measuring on a sample is that for most useful things (like measuring test/train divergence) this injects so much noise into the signal that it’s much less useful. It’s contingent more on the overall size of your dataset than a ratio. If you’ve got 100m examples, then taking a class-balanced random sample of 10m is pretty reasonable. If you’ve got 10k examples then taking a sample of 1000 is probably going to mess everything up.

Above all, think about why you’re measuring accuracy on your training set. In most cases when I see someone doing this, they don’t have a great reason for doing so beyond wanting higher accuracy numbers.

View original question on Quora >

Follow Slater on Quora >>

Increase intake capacity. Drive top line revenue growth.

Get started with Indico

1-1 Demo

Monthly Live Demo

Interactive Demo



Gain insights from experts in automation, data, machine learning, and digital transformation.

Unstructured Data Explained

Answers to the most complex questions in unstructured data.

CTO Corner

An accumulation of content straight from our co-founder and CTO.

Unstructured Unlocked

Enterprise leaders discuss how to unlock value from unstructured data.
Subscribe to our blog

Get our best content on intelligent automation sent to your inbox weekly!