MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Labeled data has both the input and output parameters in a completely machine-readable pattern, but requires a lot of human labor to label the data, to begin with. Unlabeled data only has one or none of the parameters in a machine-readable form. This negates the need for human labor but requires more complex solutions.

Detailed explanation-2: -Labeled data is used in supervised learning, whereas unlabeled data is used in unsupervised learning . Labeled data is more difficult to acquire and store (i.e. time consuming and expensive), whereas unlabeled data is easier to acquire and store.

Detailed explanation-3: -Supervised machine learning requires labelled input and output data during the training phase of the machine learning model lifecycle. This training data is often labelled by a data scientist in the preparation phase, before being used to train and test the model.

Detailed explanation-4: -Supervised Learning is the first type of machine learning, in which labelled data used to train the algorithms. In supervised learning, algorithms are trained using marked data, where the input and the output are known.

Detailed explanation-5: -Semi-supervised learning combines unsupervised and supervised learning by using a relatively small labeled training set together with a larger unlabeled training set. The labeled set provides initial training that is used to infer labels for the unlabeled data, which then can refine training.