Overfitting refers to a model that is only set for a very small amount of data and ignores the bigger picture. There are three main methods to avoid overfitting: Keep the model simple—take fewer variables into account, thereby removing some of the noise in the training data Use cross-validation techniques, such as k folds cross-validation Use regularization techniques, such as LASSO, that penalize certain model parameters if they're likely to cause overfitting