Supervised learning: regression, classification
Machine Learning · Engineering
Study notes
House prices: features (area, rooms) -> price (regression, MSE loss). Email spam: features (words) -> spam/not (classification, cross-entropy). Train on 80%, test on 20%. A model with 99% train but 70% test overfits; regularization or more data fixes it.