AdaBoost

Video you need to watch first

• AdaBoost, Clearly Explained

Key words and equation

• Stump(树桩) means classification just by one feature
• Amount of say

$$\text{Amout of say}=\frac{1}{2}\log (\frac{1-\text{Total Error}}{\text{Total Error}})$$

• Wrong Classified Sample New Weight

$$\text{New Sample Weight}=\text{Sample Weight}\times e^{\text{amount of say}}$$

• Correct Clasified Sample New Weight

$$\text{New Sample Weight}=\text{Sample Weight}\times e^{-\text{amount of say}}$$

• After reassing sample weight, do bootstrap sample based on their new weight, it will select big weight sample lots of times to adjust next model
• In last prediction, the amount of say decide which results we will pick.

Question

• why decision stumps instead of trees?