In today’s class we learnt about how we use tools like Least Squares Error (LSE) and Mean Squared Error (MSE) to measure how close our predictions are to reality. LSE helps find the best-fit line, and MSE gives us an average of how much our predictions differ from the actual values. Training Error tells us how well our model learned from the data it was trained on, while Test Error checks if our model can handle new, unseen data. It’s like checking if we studied well for a test (Training Error) and if we can apply that knowledge to questions we’ve never seen before (Test Error). Keeping an eye on these measures helps us make our models better over time.
Imagine you’re teaching a computer to make predictions, like guessing a person’s height based on some information. Least Squares Error and Mean Squared Error are like tools that help the computer learn the best way to make these guesses. Training Error is like checking how well the computer memorized the examples it learned from, while Test Error is like testing if it can correctly guess the height of someone it’s never seen before. It’s a bit like making sure the computer not only learns from its training data but can also adapt to new challenges.