 Original boosting algorithms typically used either decision stumps 
or decision trees as weak hypotheses.As an example, boosting decision stumps creates
a set of formula_1 weighted decision stumps (where formula_1
is the number of boosting iterations), which then vote on the final classification according to their weights.Individual decision stumps are weighted according to their ability to classify the data.