# How does boosting work?

What is the easiest way to understand boosting?

Why doesn't it boost very weak classifiers "to infinity" (perfection)?

The learning rate is chosen in the range $[0,1]$. Smaller values ($<0.01$) preferred. This is a weighting applied to each tree to down weight the contribution of each model to the fitted values.