Reels · 15 of 40
Reel · 51 s
Gradient boosting explained: 594 chained trees that train 9 times faster than a forest
In 51 seconds and narrated: with learning rate 1.0, gradient boosting destabilized and after 3,000 rounds ended at AUC 0.681, below a decision tree with no depth limit; it adds small trees in a chain and each one fits what the previous ones missed; with rate 0.03, validation chose 594 rounds and it reached 0.883; against a 200-tree forest the difference was not conclusive, but on one thread it trained 9 times faster and weighs 2.1 MB against 124 MB; and with one-question trees it needed almost 20,000 rounds. Muted by default: turn the sound on in the controls.
- Length
- 51 s
- Published
This reel sums up Gradient boosting explained: 594 chained trees that train 9 times faster than a forest, where the method, the tables and what did not work are.