Consider the dataset shown in Table 1 for a binary classification problem. Customer ID 1 Housing Type Gender Marital Status Apartment Male Married House Male Single Married House Female Apartment Female Single CO w Apartment Male Married Hostel Male C Single Married House Female Apartment Female Single 18 5 8 8 5 5 8 8 5 5 8 8 5 5 8 Apartment Male Male House Married Single Married Hostel Female Hostel Female Single House Male Married Hostel Male Single Hostel Married Female Female Apartment Single Table 1 a. [1.5 points) Compute the Gini index, entropy, and misclassification error for the overall data b. [6 points) Compute the Gini index, entropy, and misclassification error for each of the four attributes (consider a multi-way split using each unique value of an attribute). c. [3 points) Compute the information Gain (IG) obtained by splitting the overall data using each of the four attributes. Which attribute provides the highest IG, and which attribute provides the lowest IG. Consider the dataset shown in Table 1 for a binary classification problem. Customer ID 1 Housing Type Gender Marital Status Apartment Male Married House Male Single Married House Female Apartment Female Single CO w Apartment Male Married Hostel Male C Single Married House Female Apartment Female Single 18 5 8 8 5 5 8 8 5 5 8 8 5 5 8 Apartment Male Male House Married Single Married Hostel Female Hostel Female Single House Male Married Hostel Male Single Hostel Married Female Female Apartment Single Table 1 a. [1.5 points) Compute the Gini index, entropy, and misclassification error for the overall data b. [6 points) Compute the Gini index, entropy, and misclassification error for each of the four attributes (consider a multi-way split using each unique value of an attribute). c. [3 points) Compute the information Gain (IG) obtained by splitting the overall data using each of the four attributes. Which attribute provides the highest IG, and which attribute provides the lowest IG