- [[least squares or squared loss]], [[Python]], [[loss function]] # Idea Loss function used for classification problems. 0 for correct prediction and 1 for incorrect prediction, thus high values indicate larger errors (i.e., worse model). This loss is important for conceptual understanding, but it's very difficult to minimize (because of its categorical nature; remember, [[gradient descent]] requires continuous functions!). # References - https://campus.datacamp.com/courses/linear-classifiers-in-python/loss-functions?ex=4 # Figures - https://campus.datacamp.com/courses/linear-classifiers-in-python/loss-functions?ex=7 ![[Pasted image 48.png]]