MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The main difference between supervised and unsupervised learning: Labeled data. The main distinction between the two approaches is the use of labeled datasets. To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not.

Detailed explanation-2: -The main difference between supervised vs unsupervised learning is the need for labelled training data. Supervised machine learning relies on labelled input and output training data, whereas unsupervised learning processes unlabelled or raw data.

Detailed explanation-3: -For supervised learning to work, you need a labeled set of data that the model can learn from to make correct decisions. Data labeling typically starts by asking humans to make judgments about a given piece of unlabeled data.

Detailed explanation-4: -In a nutshell, supervised learning is when a model learns from a labeled dataset with guidance. And, unsupervised learning is where the machine is given training based on unlabeled data without any guidance.