Gradient Boosting
Gradient boosting builds one small model after another. Each new model tries to fix the last mistakes.
What it does
Gradient boosting builds one small model after another. Each new model tries to fix the last mistakes.
Boosting: Making a model stronger • Gradient: Direction of the error
How it works
- Start with a simple guess.
- Check where the guess was wrong.
- Add a new small model that focuses on those mistakes.
Uses & Applications
- Search engines can use it to rank better results higher.
- A store can predict which products may sell next week.
- A delivery company can predict which packages might arrive late.
In short
It is a chain of models where each one fixes mistakes from the one before it.
Try it yourself
Fix the mistakes one round at a time
A team of tiny models works together. Each new model tries to fix what the team still gets wrong.
Your turn
Change one setting at a time. Reset example puts everything back.
Correct residual errors
Step 1Why this happens