An example of cross validation is imagine learning to play a game, and to figure out how good you’re becoming, you decide to do a “5-round practice test.” In each round, you play the game a bit differently, learning from your mistakes and adjusting your strategy. After the 5 rounds, you look at your scores in each round and calculate the average score. This average score gives you a sense of how well you’re doing overall. In machine learning, we do something similar with cross-validation. Instead of training a model just once, we train it several times on different parts of our data, checking how well it does on average. It’s like making sure the model doesn’t just remember the training data but can also handle new situations effectively.
An example of bootstrap method is if you want to know the average height of students in a school, but measuring everyone is too time-consuming. Here’s where the bootstrap idea comes in. Instead of measuring every student, we randomly pick some students, record their heights, and put them back. Repeat this process several times, noting heights each time. By looking at all these recorded heights, we can make a good guess about the average height of all students. Bootstrap is like taking sneak peeks at smaller groups, putting them back into the “height pool,” and using those glimpses to figure out the average height without measuring every single student. It’s a clever way to estimate things without doing the whole task, making it much more practical, especially with large groups of data.