Training Data: Why Quality Beats Quantity
Whenever a model learns from examples, the examples shape what it becomes. Poor data produces poor results no matter how clever the algorithm, which is why we spend so much effort here.
This article explains what good training data looks like and why more is not always better.
The Old Saying Still Holds
Rubbish in, rubbish out. A model trained on biased, outdated or messy data will reproduce those flaws, often invisibly until something goes wrong.
What Good Data Looks Like
- Accurate and consistently labelled.
- Representative of the real situations the model will face.
- Free of obvious bias against any group.
- Recent enough to reflect how things work today.
Why Cleaning Comes First
It is tempting to rush to training, but time spent cleaning data almost always pays back in better, more reliable results.
- Gather and review a sample for obvious problems.
- Fix labels, remove duplicates and fill gaps.
- Check the mix is balanced and fair.
- Only then train, so effort is not wasted.
Frequently Asked Questions
Is more data always better?
No. A smaller, clean and representative dataset usually beats a larger messy one, because the model learns the right lessons.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.