You will use the Appliances energy prediction data set. You should ignore the first attribute, which is a date-time variable, and you should also remove the last attribute, which is a duplicate of the previous one. Use the first attribute (after removing the date-time variable), which denotes the Appliances Energy Use, as the response variable, with the remaining attributes as predictor variables.
Note that the Appliances Energy Use attribute takes values in the range . However, for binary classification, we need only two values, so for
[10, 1080]
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 (-1).
You should shuffle the data points before selecting 70% of the data training and 30% for testing, so that there is an equal mix of the classes in both.
You will implement the dual SVM Algorithm 21.1 (Chapter 21, page 540). You must implement both the "hinge" and "quadratic" loss cases, which will be command line options.
CSCI4390
You must implement two kernels, namely, both linear and Gaussian.
CSCI6390
You must implement three kernels, namely, linear, Gaussian, and polynomial.
