Supervised Learning - Classification Week 8 Challenge

2)

Advantages of KNN:

1. Simple to understand and implement: KNN is a straightforward and easy-to-understand algorithm that is simple to implement.

2. Non-parametric: KNN is a non-parametric algorithm, which means it does not make any assumptions about the underlying data distribution.

3. No training phase: KNN does not require a training phase. It simply memorizes the entire training dataset, making it a lazy learner.

4. Works well with small datasets: KNN is particularly useful when working with small datasets as it can efficiently classify or regress data without needing large amounts of training data.

Disadvantages of KNN:

1. Computationally expensive: KNN can be computationally expensive when working with large datasets, as it requires a search of the entire dataset for each prediction.

2. Sensitivity to irrelevant features: KNN can be sensitive to irrelevant features in the dataset, which can affect its accuracy.

3. Curse of dimensionality: KNN can suffer from the curse of dimensionality, which is a phenomenon where the performance of the algorithm deteriorates as the number of features or dimensions in the dataset increases.

4. Need to choose the optimal value of K: KNN requires choosing the optimal value of K, which can be difficult and time-consuming, and can significantly affect the performance of the algorithm.