- \gamma f_{2}(x)$$`

$y \approx \gamma$ ![:scale 22.5%](images/grad_boost_term_1.png) + $\gamma$ ![:scale 22.5%](images/grad_boost_term_2.png) + $\gamma$ ![:scale 20%](images/grad_boost_term_3.png) + ...
<br />
<br />
Learning rate $\gamma, i.e. 0.1$

???
- Iteratively add regression trees to model
- Use log loss for classification
- Discount update by learning rate

FIXME plot for regression models
Come back to this
---
class: compact
# Gradient Boosting is Gradient Descent

.left-column[
## Linear regres