Breaking Down Cost Functions in Linear Regression: A Conceptual Overview

The cost function in linear regression quantifies the error between predicted and actual values. By minimizing this cost, the model improves accuracy. The formula involves the squared difference between predictions and actual values, divided by 2m (number of data points). Squaring emphasizes larger errors, while the division normalizes the cost regardless of the dataset size. The cost function helps find the optimal values for model parameters (w and b) that minimize error. Different applications may require different cost functions tailored to their specific characteristics.