Question

What are the pros and cons of the KNN algorithm?

Answer 1

Pros:

• Simple to understand and implement.
• No assumptions about the data distribution.
• Suitable for multi-class classification and regression tasks.
• Works well with a small number of features.

Cons:

• Computationally expensive for large datasets.
• Sensitive to irrelevant and redundant features.
• Requires a proper distance metric, which may be challenging to choose for some data types.
• Prone to the curse of dimensionality for high-dimensional data.