Problem 3. Decision Trees (20 points) Consider a binary class classification problem with following data pionts: 1 1 25 -1 10 1 7 1 1 12 1 19+ 1 6 1 1 22 18+ 1 1 7- In this problem we are going to build three (binary) decision trees based on three different impurity measures (a) Use the entropy impurity to create by hand a decision tree classifier for this data. (b) Use the error rate to create a decision tree classifier for this data (c) Find the optimal (smallest height) decision tree for this data which perfectly classify all data points (d) Write your conclusion from previous parts. Problem 3. Decision Trees (20 points) Consider a binary class classification problem with following data pionts: 1 1 25 -1 10 1 7 1 1 12 1 19+ 1 6 1 1 22 18+ 1 1 7- In this problem we are going to build three (binary) decision trees based on three different impurity measures (a) Use the entropy impurity to create by hand a decision tree classifier for this data. (b) Use the error rate to create a decision tree classifier for this data (c) Find the optimal (smallest height) decision tree for this data which perfectly classify all data points (d) Write your conclusion from previous parts