Class Activity: Adaboost

Let us assume that for a binary classification problem we have the following dataset:

Dashed line is the first weak classifier used in Adaboost. What is the importance $$ assigned to it$?$ Update all the weights. Write down your answers in the text area.

Class Activity: Bagging

Let us assume that for a binary classification problem, we have a dataset with $5$ data points: $A,B,C,D,$ and $E$ where A and $B$ are positive samples and the other three ones are negatives.

We use bagging and during the bootstrapping process, every time we just select one of the data points:

Bootstrap $1$: $A,A,A,A,A$

Bootstrap $2$: $B,B,B,B,B$

Bootstrap $3$: C$,$ C$,$ C$,$ C$,$ C

Bootstrap $4$: D$,$ D$,$ D$,$ D$,$ D

Bootstrap $5$: E$,$ E$,$ E$,$ E$,$ E

What is the training performance of the Bagging classifier? Calculate accuracy, precision, and recall. Do you think if this may result in an improvement compared with the traditional non$-$ensemble method?