Definition
What if half the labels in your examples were wrong?
Good data is accurate, complete, varied and relevant to the task. Bad data has problems such as too few examples, wrong labels, missing values, duplicates or a one-sided mix, and a model trained on it learns those problems.
In simple words
Engineers say "garbage in, garbage out". If you teach a model with photos where some cats are labelled dogs, it will learn the wrong thing. If all your dog photos are taken in sunshine, it may think 'sunny' means 'dog'. Checking the data is as important as building the model.
Why it matters
Most AI failures in the real world trace back to the data, not the model. Learning to spot data problems early saves time and prevents unfair or dangerous mistakes later.
How it works
| Problem | Example | What the model learns |
|---|---|---|
| Too little data | Only 5 photos of each animal | A weak, unreliable pattern |
| Wrong labels | Some cats marked as dogs | Confused answers |
| Missing values | Size left blank for many animals | Gaps in its knowledge |
| Duplicates | The same photo added ten times | Too much weight on one case |
| One-sided data | Only big dogs, never small ones | Mistakes on whatever is missing |
Try it yourself
Clean a messy dataset
A shop wants to predict sales. See what's wrong with its data before training.
Find the problems in a dataset
Each row comes from a dataset for a fruit-sorting model. What is wrong with it, if anything?
Orange, 155 g, bumpy skin. Label: apple
Red, 150 g, smooth skin. Label: orange
Row 7 is exactly the same as row 6
Green, 140 g, smooth skin. Label: apple
Orange, ___ g, bumpy skin. Label: orange
Common mistakes
- Assuming data is correct because a computer stored it.
- Collecting data only from what is easy to find.
- Fixing the model when the real problem is the data.
Check yourself
Summary
- Good data is accurate, complete, varied and relevant.
- Watch for wrong labels, missing values, duplicates and one-sided data.
- Most AI failures come from data problems.
Finished reading?
Sources: Google Machine Learning Crash Course