whats the difference between "True. Boosting can converge to a classifier with zero training error, especially if the weak classifiers are able to provide a small edge $($error rate less than

$\backslash$epsi

$)$ over random guessing and the data is separable by the hypotheses in H$.$ By iteratively focusing on the hardest examples, boosting can drive the training error down.

$($c$)$ True. Boosting focuses on minimizing training error by re$-$weighting the training examples. It tends to converge towards the classifier in H that has the smallest possible training error because it iteratively selects the hypothesis that best corrects the errors of the combined classifier from previous rounds."