Quiz · What Machine Learning Is · lesson 5 of 9Quiz — supervised and unsupervisedHow does supervised learning differ from unsupervised learning?[A]In supervised learning an engineer picks the model, features and hyperparameters by hand. Unsupervised learning is AutoML: the system chooses the model, features and settings by itself, with no person involved in setting up the training run.[B]Both kinds train on labeled examples. Supervised learning uses the labels to predict a number or a class, while clustering uses the same labels to decide which group each new example should join.[C]Supervised learning trains once on a fixed dataset and is then frozen. Unsupervised learning keeps updating itself on live data after release, so it never needs retraining or a held-out test set to check it.[D]Supervised learning trains on examples paired with a known target, such as a price or a spam label, and learns to predict it. Unsupervised learning gets no target and finds structure in the features alone, such as clusters.Subscribe to take the courseThe course is free. Sign in with Telegram and subscribe to the channel to open every lesson.Continue with TelegramSign in another way← BackLearning without answersNext →Classification and regression
