ML Models Spill: We're All Just Guessing Electricity Bills – MSE Yells Louder at ₹500 Blunders Than ₹20 Whoopsies

In a recent explanation of machine learning concepts, a key aspect of model evaluation was discussed, specifically the role of errors and loss functions in measuring the accuracy of predictions. The example of guessing a monthly electricity bill was used to illustrate the concept of error, which is the difference between a predicted value and the actual value. The article highlighted that every prediction has an error, and machine learning models aim to be less wrong over time. The concept of loss functions, such as Mean Squared Error (MSE), was introduced as a way to evaluate the overall performance of a model, with MSE being widely used in linear regression due to its ability to punish big mistakes more severely. The article concluded by setting the stage for the next topic, gradient descent, which is a method used to reduce loss and improve model performance. This explanation is part of a series on beginner-friendly AI topics.