MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -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-2: -Why is the k-nearest neighbors algorithm called “lazy”? Because it does no training at all when you supply the training data.

Detailed explanation-3: -In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries.

Detailed explanation-4: -KNN is a lazy learning, non-parametric algorithm. It uses data with several classes to predict the classification of the new sample point. KNN is non-parametric since it doesn’t make any assumptions on the data being studied, i.e., the model is distributed from the data.

Detailed explanation-5: -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.