CSCI4390: Binary Classification
You will implement binary classification via the MLP training Algorithm 25.1 (Chapter 25, page 655). This algorithm assumes sigmoid activation for hidden layer, and squared error for the loss. However you have to use ReLU activation for the hidden layer, and use binary cross entropy for the loss. Consequently, you have to modify lines 10 and 11 appropriately so that line 10 uses cross-entropy loss, and line 11 uses ReLU derivative. See section 25.1.1 for derivative of ReLU, and section 25.2.3 for the derivative of binary cross-entropy loss function.
Note that the Appliances Energy Use attribute takes values in the range [10, 1080]. However, for binary classification, 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 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. CSCI6390: Multiclass Classification
You will implement multiclass classification via the deep MLP algorithm in Algorithm 25.2 (Chapter 25, page 668). You should use ReLU activation for all hidden layers, and softmax for the output layer. See section 25.1.1 for the derivative of the activation functions, and end of section 25.4.3 for derivative of the multiclass cross-entropy function.
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.
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. What to submit