What is the KNN algorithm and how does it work?

What is the KNN algorithm and how does it work?

The KNN algorithm, or K-Nearest Neighbors, is a simple, supervised machine learning algorithm used for classification and regression. It works by finding the 'k' closest data points to a given input and making predictions based on the majority class or average of those neighbors.

How do I choose the value of k?

Choosing the value of k is crucial. A smaller k can be noisy and lead to overfitting, while a larger k can smooth out the predictions but may ignore local patterns. A common approach is to use cross-validation to find the optimal k.

What are some advantages and disadvantages of KNN?

Advantages of KNN include its simplicity and effectiveness for small datasets. Disadvantages include its computational cost for large datasets and sensitivity to irrelevant features.

Can KNN be used for regression tasks?

Yes, KNN can be used for regression tasks by averaging the values of the k nearest neighbors instead of classifying them.