Load the iris sample dataset from sklearn (load iris()) into Python using a Pandas dataframe. Induce a set of binary Decision Trees with a minimum of 2 instances in the leaves, no splits of subsets below 5, and an maximal tree depth from 1 to 5 (you can leave other parameters at their defaults). Which depth values result in the highest Recall? Why? Which value resulted in the lowest Precision? Why? Which value results in the best F1 score? Explain the dierence between the micro/macro/weighted methods of score calculation.