Supervised Learning - Classification Week 8 Challenge

2) Pros and cons of K-Nearest Neighbours

Pros:

• Simple to understand and easy to implement.
• It is a non-parametric algorithm and hence, there is no assumptions.
• It can be used for classification as well as regression.
• There is flexibility for the user to select distance while building the model such as Euclidean Distance, Hamming Distance, Manhattan Distance, Minkowski Distance.

Cons:

• Computationally expensive as it searches for nearest neighbours for the new data point during prediction stage.
• Memory intensive as it stores all the data points.
• Highly sensitive to outliers and its accuracy is impacted by noise or irrelevant data.