Consider the following datapoints $x$ and labels $y$ :

$x=([0,1,2,3,4]).$

You want to do boosting and therefore aggregate several weak classifiers. Following the AdaBoost algorithm you modify the weights of the data points and the weights of the models in the ensemble in each iteration step.

Assume that the first model all data which are greater than $1\mathrm{.}5$ with $1$ and $0$ otherwise. Mark the chariges of the weights of the individual data points:

Point $x=0$ :

Point $x=1$ :

Point $x=2$ :

Point $x=3$ :

Point $x=4$

In the second iteration step, we use a model which classifies datapoints which are greater than $2\mathrm{.}5$ with $0$ and $1$ otherwise. Mark the changes of the weights of the individual data points:

Point $x=0$ :

Point $z=1$ :

Point $z=2$

Foint $x=3$

Point $x=4$

in the final aggregation, which model is assigned the larger neight?