build models that can learn representations from our training data that's still generalize to unseen test data now assume that you want to build a line that best describes these points you can see on the on the screen under fitting describes if we if our model does not describe the state of complexity of this problem or if we can't really capture the true complexity of this problem while overfitting on the right starts to memorize certain aspects of our training data and this is also not desirable we want the middle ground