Load the Breast Cancer Wisconsin (Diagnostic) sample dataset from the UCI Machine Learning Repository (The discrete version at: breast-cancerwisconsin.data) into Python using a Pandas dataframe. Induce a binary Decision Tree with a minimum of 2 instances in the leaves, no splits of subsets below 5, and a maximal tree depth of 2 (use the default Gini criterion). Calculate the Entropy, Gini, and Misclassification Error of the first split - what is the Information Gain? What is the feature selected for the first split, and what value determines the decision boundary?