Supervised Learning - Classification Week 8 Challenge

Ans 2. Pros:

a) Simple to implement.

b) KNN can be used for classification, regression, and feature selection tasks.

c) KNN can handle non-linear relationships between features.

Cons:

a) KNN can be computationally expensive, especially for large datasets.

b) The choice of k (number of nearest neighbors) significantly affects the algorithm's performance.

c) KNN is sensitive to feature scaling, and standardization/normalization may be necessary.