Define the concept of ‘loss function’ with suitable expressions for different cases.

In the field of machine learning and statistical modeling, a loss function is a mathematical function that measures the discrepancy between predicted values and true values. It quantifies the "loss" or "error" associated with the predictions made by a model. The goal is to minimize the loss function to improve the accuracy and performance of the model. The choice of the loss function depends on the specific task and the nature of the data. Here are some examples of loss functions for different cases:

1. Mean Squared Error (MSE):
   The mean squared error is a commonly used loss function for regression problems, where the goal is to predict continuous values. It measures the average squared difference between the predicted values (ŷ) and the true values (y).

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