MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -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-2: -KNN is a non-parametric, slow learning algorithm. It predicts the categorization of a new sample point using data from many classes. KNN is non-parametric since it makes no assumptions about the data it is analyzing, i.e. the model is distributed from the data.

Detailed explanation-3: -KNN is one of the simplest forms of machine learning algorithms mostly used for classification. It classifies the data point on how its neighbor is classified. KNN classifies the new data points based on the similarity measure of the earlier stored data points. For example, if we have a dataset of tomatoes and bananas.

Detailed explanation-4: -KNN is a non-parametric and lazy learning algorithm. Non-parametric means there is no assumption for underlying data distribution. In other words, the model structure determined from the dataset.

Detailed explanation-5: -What is KNN? K-Nearest Neighbors is one of the simplest supervised machine learning algorithms used for classification. It classifies a data point based on its neighbors’ classifications. It stores all available cases and classifies new cases based on similar features.