# What Does the Normalization Factor Mean in the AdaBoost Algorithm?

I am studying the AdaBoost algorithm. The update rule for a weak hypothesis is:

$Dt+1(i) = Dt(i)exp(−αtyiht(xi))/zt$

where $zt$ is a normalization factor chosen so that $Dt+1$ is a distribution.

What does the 'normalization factor' mean? Could I have an explanation with an example, please?

Say your unnormalized value is [0.1, 0.2, 0.3, 0.2]. You normalize it by dividing it by $z_t=0.1+0.2+0.3+0.2=0.8$, therefore get the normalized value [0.125, 0.25, 0.375, 0.25] which sums up to 1..