K-Nearest Neighbors (KNN)

Overview

• KNN is a simple supervised machine learning algorithm.
• Applicable for both classification and regression problems.

Example Explanation

• Consider a two-dimensional example for better understanding.
• Objective: Classify a given point into one of three groups.

Steps to Implement KNN

1. Calculate Distances:

   • Measure the distance between the given point and other points.
   • Common Distance Function: Euclidean distance.

2. Sort Neighbors:

   • Sort the neighbors by distance in increasing order.

3. Classification:

   • Classify the point by majority vote of its k-nearest neighbors.
   • Assign the point to the most common class among its neighbors.

4. Key Value (k):

   • Controls the balance between overfitting and underfitting.
   • Optimal k can be determined using cross-validation and learning curves.
   • Trade-offs:
     • Small k: Low bias, high variance.
     • Large k: High bias, low variance.
     • Important to find a balanced k.

5. Regression:

   • For regression, return the average of the k nearest neighbors' labels as the prediction.

Code Example

• Uses the Ares dataset with the first two features for demonstration.
• KNN algorithm implemented using sklearn (self-explanatory).
• Encouraged to experiment with different parameters.

Visualization

• Two plots from the code example:
  • Left Plot: Classification decision boundary with k=15.
  • Right Plot: Classification decision boundary with k=3.

Conclusion

• This lecture is part of the Betasize ML concept from Intuitive Machine Learning.
• Encourage viewers to comment, like, and subscribe for more learning content.