MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -K-Nearest Neighbors (K-NN) is one of the simplest machine learning algorithms. When a new situation occurs, it scans through all past experiences and looks up the k closest experiences.

Detailed explanation-2: -KNN is a typical example of a lazy learner. It is called lazy not because of its apparent simplicity, but because it doesn’t learn a discriminative function from the training data but memorizes the training dataset instead.

Detailed explanation-3: -The k-nearest neighbors algorithm, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.

Detailed explanation-4: -K-NN is a classification or regression machine learning algorithm while K-means is a clustering machine learning algorithm. K-NN is a lazy learner while K-Means is an eager learner.

Detailed explanation-5: -Lazy learning algorithms do not try to build a model until they are given new data. This contrasts with eager learning algorithms, which try to build a model as soon as they are given training data. Lazy learning is a good choice when the training data is not too large and the cost of learning is high.