The correct answer is: C. it can be used in both classification and regression.
K-nearest neighbors (KNN) is a supervised learning algorithm that can be used for both classification and regression tasks. In classification, KNN assigns a class label to an instance by finding the k nearest neighbors of the instance in the training data and then assigning the class label that is most common among the k nearest neighbors. In regression, KNN predicts a value for an instance by finding the k nearest neighbors of the instance in the training data and then averaging the values of the k nearest neighbors.
KNN is a simple and easy-to-understand algorithm that is often used as a baseline algorithm for comparison with other algorithms. It is also a robust algorithm that can be used with a variety of data types. However, KNN can be computationally expensive, especially for large datasets.
Here is a brief explanation of each option:
- Option A: KNN can be used for classification. In classification, KNN assigns a class label to an instance by finding the k nearest neighbors of the instance in the training data and then assigning the class label that is most common among the k nearest neighbors.
- Option B: KNN can be used for regression. In regression, KNN predicts a value for an instance by finding the k nearest neighbors of the instance in the training data and then averaging the values of the k nearest neighbors.
- Option C: KNN can be used in both classification and regression.
- Option D: KNN is not useful in ML algorithm. This is not true. KNN is a supervised learning algorithm that can be used for both classification and regression tasks.