KNN (K-Nearest Neighbors) - Classification/Regression

KNN (K-Nearest Neighbors) is primarily a classification algorithm, though it can also be used for regression.

How KNN Classification works:

Choose a value for K (number of neighbors to consider)
For a new data point, find the K closest points in the training data (using distance measures like Euclidean distance)
Assign the class based on majority voting among those K neighbors

Example: If K=5 and 3 of the 5 nearest neighbors are "Cat" and 2 are "Dog," the new point is classified as "Cat."

Key characteristics:

Lazy learner — it doesn't build a model during training; it just stores the data and does all the work at prediction time
Non-parametric — makes no assumptions about the underlying data distribution
Instance-based — uses actual training instances to make predictions

KNN for Regression:

When used for regression, instead of voting, it takes the average (or weighted average) of the K neighbors' values to predict a continuous output.

So while KNN is most commonly associated with classification tasks, it's a versatile algorithm that handles both classification and regression depending on how you aggregate the neighbors' outputs.

KNN Classification (the classic version):

Target labels are discrete/categorical (e.g., "Spam" or "Not Spam")
Uses majority voting among K neighbors
Output: a class label

KNN Regression (adapted version):

Target values are continuous/numerical (e.g., house price, temperature)
Uses averaging of K neighbors' values instead of voting
Output: a continuous number

Same core idea, different aggregation:

The "find K nearest neighbors" part is identical. The difference is what you do with those neighbors:

	Classification	Regression
Target	Discrete labels	Continuous values
Aggregation	Majority vote	Mean/weighted average
Example	Predict "Cat" or "Dog"	Predict price = $250,000

So you're essentially correct:

The original and most common use of KNN is classification with discrete labels. The regression variant is an adaptation that borrows the neighbor-finding mechanism but replaces voting with averaging. Some people consider them two separate algorithms that share the same "nearest neighbors" framework.

In practice, when someone says "KNN" without qualification, they usually mean classification.

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