Definition
The model says "67 per cent dog". What does that mean?
A prediction is the answer a model gives for a new case. Confidence is how sure the model is about that answer, often shown as a percentage. High confidence does not guarantee the answer is right.
In simple words
Our animal model looks at the three most similar examples. If all three are dogs, it says dog, 100 per cent sure. If two are dogs and one is a cat, it says dog, 67 per cent sure. Like a weather forecast of 70 per cent chance of rain, it tells you how much to rely on the answer.
Why it matters
Confidence helps decide when to trust AI and when a person should check. A hospital might let AI handle cases it is very sure about and send uncertain ones to a doctor. But a model can be confidently wrong, especially on cases unlike its training data.
How it works
| Confidence | What to do |
|---|---|
| Very high (above 90%) | Usually accept, but still check a sample |
| Medium (60 to 90%) | Use with care; check important cases |
| Low (below 60%) | Ask a person to decide |
Try it yourself
Read the confidence bars
Pick each new animal and read how sure the model is. Find one where it is only 67 per cent sure, and work out why.
Common mistakes
- Thinking 100 per cent confidence means certainly right.
- Ignoring low confidence. That is the model asking for help.
- Thinking confidence and accuracy are the same. Accuracy is measured on a test set.
Check yourself
Summary
- A prediction is the model's answer; confidence is how sure it is.
- Use confidence to decide when a person should check.
- High confidence is not proof: models can be confidently wrong.
Finished reading?