K-Nearest Neighbors Visualizer

How K-Nearest Neighbors Works

K-Nearest Neighbors (KNN) is a simple, instance-based learning algorithm that classifies new data points based on their similarity to existing data points.

Algorithm Steps:

1. Store all training data points
2. When classifying a new point:
   1. Calculate distances to all stored points
   2. Select the k nearest points
   3. Assign the most common class among these neighbors

Key Parameters:

• k: Number of neighbors to consider (small k = more complex boundaries)
• Distance Metric: How to measure similarity between points

Characteristics:

• No explicit training phase (lazy learning)
• Decision boundaries can be highly non-linear
• Sensitive to the choice of k and distance metric
• Works well for small to medium datasets