MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. It’s easy to implement and understand, but has a major drawback of becoming significantly slows as the size of that data in use grows.

Detailed explanation-2: -As we saw above, KNN algorithm can be used for both classification and regression problems. The KNN algorithm uses ‘feature similarity’ to predict the values of any new data points. This means that the new point is assigned a value based on how closely it resembles the points in the training set.

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.

Detailed explanation-4: -Yes, KNN can be used for regression problem statements. In other words, the KNN algorithm can be applied when the dependent variable is continuous. For regression problem statements, the predicted value is given by the average of the values of its k nearest neighbours.

Detailed explanation-5: -KNN regression is a non-parametric method that, in an intuitive manner, approximates the association between independent variables and the continuous outcome by averaging the observations in the same neighbourhood.