[amp_mcq option1=”To build decision trees” option2=”To classify data points based on neighbors” option3=”To visualize data relationships” option4=”To fit linear regression models” correct=”option2″]
The correct answer is B. To classify data points based on neighbors.
K-nearest neighbors (KNN) is a supervised machine learning algorithm that can be used for both classification and regression tasks. It is a simple algorithm that works by finding the k nearest neighbors of a given data point and then assigning the data point to the class that is most common among its neighbors.
KNN is a non-parametric algorithm, which means that it does not make any assumptions about the underlying distribution of the data. This makes it a versatile algorithm that can be applied to a wide variety of problems.
KNN is also a relatively efficient algorithm, which makes it suitable for large datasets.
However, KNN can be sensitive to the choice of k. If k is too small, the algorithm may be overfitting the data and will not generalize well to new data. If k is too large, the algorithm may be underfitting the data and will not be able to capture the nuances of the data.
Overall, KNN is a simple, versatile, and efficient machine learning algorithm that can be used for both classification and regression tasks.
Option A is incorrect because decision trees are a type of supervised machine learning algorithm that is used for classification tasks.
Option C is incorrect because visualization is not the primary purpose of KNN. KNN can be used to visualize data relationships, but this is not its primary purpose.
Option D is incorrect because KNN is not a linear regression model. Linear regression is a type of supervised machine learning algorithm that is used for regression tasks.