it can be used for classification
it can be used for regression
it can be used in both classification and regression
not useful in ml algorithm
Answer is Right!
Answer is Wrong!
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.