Predicting a monthly electricity bill illustrates how error measures the gap between a guess and reality. In machine learning, every prediction carries an error, and the goal is to reduce it. Aggregating errors into a single score yields a loss function, the metric that guides model improvement.

An absolute error ignores direction, focusing only on magnitude. A loss function acts like a report card, summarizing all mistakes into one number; lower values signal better performance. In linear regression, the algorithm evaluates many candidate lines, selects the one with the lowest loss, and thus the best fit.

Mean Squared Error (MSE) amplifies large mistakes, making the model penalize big deviations more heavily. This aligns with human intuition and drives the selection of a line that minimizes costly errors. The next step is training, where gradient descent iteratively adjusts parameters to lower the loss. This approach underpins many regression tasks in finance, healthcare, and forecasting.