CSCI4390: Binary Logistic Regression
You will implement the binary logistic regression algorithm as described in Algorithm 24.1 (Chapter 24, page 628).
Note that the Appliances Energy Use attribute takes values in the range [10, 1080]. However, for binary regression, we need only two values, so for the purpose of this assignment you should consider energy use less than or equal to 50 as the positive class (1), and energy use higher than 50 as negative class (0). You need to do this conversion to create the binary response variable, before you select the train (70%) and test (30%) subsets.
CSCI6390: Multiclass Logistic Regression
You will implement the binary logistic regression algorithm as described in Algorithm 24.2 (Chapter 24, page 634).
Note that the Appliances Energy Use attribute takes values in the range [10, 1080]. However, for multiclass regression, we will convert these into four classes as follows: energy use less than or equal to 30 is class c1, energy use greater than 30 but less than or equal to 50 is class c2, energy use greater than 50 but less than or equal to 100 is class c3, and finally energy use higher than 100 is class c4. You need to do this conversion to create the categorical response variable, before you select the train (70%) and test (30%) subsets.
