Question

You are a designing a machine learning model for a binary classification problem. The model has three features: f1, f2, f3. Derive the objective and loss function for this problem.

Answer 1

Step-by-step explanation:

The objective function for this problem would be to accurately predict the binary output class (either 0 or 1) based on the values of the three features f1, f2, and f3.

The loss function could be a binary cross-entropy loss, which is defined as:

Loss = -(y * log(p) + (1-y) * log(1-p))

where y is the true binary class (0 or 1) and p is the predicted probability of the positive class (class 1). This loss function punishes the model more for incorrect predictions of the positive class (class 1) and rewards it for correct predictions. The goal is to minimize the loss function, which would indicate a more accurate model.