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: -In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

Detailed explanation-3: -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”.

Detailed explanation-4: -Semi-supervised learning: Training data includes a few desired outputs.

Detailed explanation-5: -To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not. In supervised learning, the algorithm “learns” from the training dataset by iteratively making predictions on the data and adjusting for the correct answer.