Data Science, Regularization, Machine Learning, Editors Pick, Hands On Tutorials.
happens when you have a small training set, too small to showcase all the nuance and patterns in the data. With only a small amount of data to learn from, the model ends up hyper-focusing only on the patterns it can see in the training set. As a consequence, the model is not generalizable, meaning