When you want to understand classification and regression models, it's important to look at different ways to measure how well they perform.

For Classification Models:

1. Accuracy: This tells us how often the model gets the right answer.
2. Precision: This measures how many of the positive results were actually correct.
3. Recall: This shows how many of the actual positive cases the model was able to identify.
4. F1 Score: This combines precision and recall. It’s especially helpful when the data is unevenly distributed.

For Regression Models:

1. Mean Absolute Error (MAE): This is the average of how wrong the predictions are, without considering if they were higher or lower.
2. Mean Squared Error (MSE): This is similar to MAE, but it squares the differences. This means it pays more attention to bigger mistakes.
3. R-squared ($R^2$): This tells us what percentage of the changes in the outcome can be explained by the model.

Each of these measurements gives us helpful information based on the problem we are trying to solve!