K-nearest neighbors is a non-parametric statistical method, which predicts based on the *k* nearest neighbors. k-NN classifiers are an example of what's called instance based or memory based supervised learning.

It can be applied to both classification and regression problems.
- When applied to classification problems, the prediction result would be the most common one among the *k* nearest training observations.
- When applied to regression problems, the prediction result would be the average of the *k* nearest training observations.

Some key notes about this method:
- It makes few assumptions about structure of data
- It gives potentially accurate but sometimes unstable predictions 
- It is sensitive to small changes in training data



K-nearest Neighbors (k-NN)

Instance based learning methods work by memorizing the labeled examples that they see in the training set. And then they use those memorized examples to classify new objects later.

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