Question

What is a loss function? Write the two categories of loss functions.

Hint:

Tip: Regression → predict numbers, Classification → predict classes.

Answer 1

A loss function quantifies the discrepancy between a machine learning model's predictions and the actual target values.
It computes the error between the predicted and true outputs.
During training, the objective is to reduce this loss, thereby enhancing model accuracy.
Loss functions direct the learning process by modifying weights and parameters.
Loss functions are broadly categorized into two types:
1. Regression Loss Functions — Applicable to continuous output variables.
Illustrative examples are Mean Squared Error (MSE) and Mean Absolute Error (MAE).
2. Classification Loss Functions — Applicable to categorical or discrete output variables.
Examples include Cross-Entropy Loss and Hinge Loss.
The appropriate selection of a loss function is critical for effective model learning tailored to the specific problem type.