K-Nearest Neighbors (KNN)

K-Nearest Neighbors (KNN) is one of the simplest machine learning algorithms used for both classification and regression. It belongs to the family of instance-based, non-parametric learning algorithms.

The core idea behind KNN is that similar data points tend to have similar outputs. For a new data point, the algorithm finds the K closest data points (neighbors) in the training set and uses their values to predict the output for the new point.

Key Characteristics:

• Non-parametric: KNN doesn't make assumptions about the underlying data distribution.
• Lazy learning: KNN doesn't build a model during training; it simply stores the training data.
• Instance-based: Predictions are made based on the similarity between instances.
• Versatile: Can be used for both classification and regression tasks.

How It Works:

1. Calculate the distance between the new point and all points in the training data.
2. Select the K nearest points based on the calculated distances.
3. For classification: Assign the most common class among the K neighbors.
4. For regression: Calculate the average (or weighted average) of the K neighbors' values.