Q9) [20 points] Consider the training data set collected by the ABC bank as shown in the following Table. The last column is the target value (label). We would like to build a decision tree to classify new customers as High or Low credit customers. Consider the following two candidate splits at the root i. ii. using the Savings attribute only. using the Assets attribute only. (a) Draw a decision tree for each of the above candidate splits (b) Calculate the Gini index of the split. Based on your calculation of the Gini index, which split is better? (c) Calculate the Entropy index of the split. Based on your calculation of the Entropy index, which split is better? (Note: For simplicity, use Logio in your calculations) Customer Savings Assets Credit 1 Medium High High 2 Low Low Low 3 High Medium Low 4 Medium Medium High 5 Low Medium High 6 High High High 7 Low Low Low 8 Medium Medium High Q9) [20 points] Consider the training data set collected by the ABC bank as shown in the following Table. The last column is the target value (label). We would like to build a decision tree to classify new customers as High or Low credit customers. Consider the following two candidate splits at the root i. ii. using the Savings attribute only. using the Assets attribute only. (a) Draw a decision tree for each of the above candidate splits (b) Calculate the Gini index of the split. Based on your calculation of the Gini index, which split is better? (c) Calculate the Entropy index of the split. Based on your calculation of the Entropy index, which split is better? (Note: For simplicity, use Logio in your calculations) Customer Savings Assets Credit 1 Medium High High 2 Low Low Low 3 High Medium Low 4 Medium Medium High 5 Low Medium High 6 High High High 7 Low Low Low 8 Medium Medium High