Supervised Learning - Classification Week 8 Challenge

Q2. Advantage:

• It's easy to understand and simple to implement
• It can be used for both classification and regression problems
• It's ideal for non-linear data since there's no assumption about underlying data
• It can naturally handle multi-class cases
• It can perform well with enough representative data

Diadvantage:

• Associated computation cost is high as it stores all the training data
• Requires high memory storage
• Need to determine the value of K
• Prediction is slow if the value of N is high
• Sensitive to irrelevant features