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unit 4/04_KNN_Algorithm.md

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+# K-Nearest Neighbors (KNN)
+
+**KNN** is a simple, "Lazy" learning algorithm.
+
+## How it Works
+1. Store all training data.
+2. When a new item arrives, find the **K** closest items (neighbors) to it.
+3. Check the class of those neighbors.
+4. Assign the most common class to the new item.
+
+## Key Concepts
+- **Lazy Learner**: It doesn't build a model during training. It waits until it needs to classify.
+- **Distance Measure**: How do we measure "closeness"?
+  - **Euclidean Distance**: Straight line distance (most common).
+  - **Manhattan Distance**: Grid-like distance.
+- **Choosing K**:
+  - If K is too small (e.g., K=1), it's sensitive to noise.
+  - If K is too large, it might include points from other classes.
+  - Usually, K is an odd number (like 3, 5) to avoid ties.
+
+## Example
+- New Point: Green Circle.
+- K = 3.
+- Neighbors: 2 Red Triangles, 1 Blue Square.
+- Result: Green Circle is classified as **Red Triangle**.