MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.

Detailed explanation-2: -With supervised learning you use labeled data, which is a data set that has been classified, to infer a learning algorithm. The data set is used as the basis for predicting the classification of other unlabeled data through the use of machine learning algorithms.

Detailed explanation-3: -Answer-A) PCA Is not supervised learning.

Detailed explanation-4: -Supervised learning: predicting an output variable from high-dimensional observations. Supervised learning consists in learning the link between two datasets: the observed data X and an external variable y that we are trying to predict, usually called “target” or “labels”.