# Machine learning/Classification algorithms

Jump to navigation
Jump to search

Classification is a subcategory of supervised learning problems.

## k-nearest neighbor[edit | edit source]

- a simple classification algorithm
**Intuition:**Find the majority vote in the training data- This is a discriminative model, meaning that there is no way to generate the training data points

### Algorithm[edit | edit source]

- Define some distance metric or similarity metric. The simplest case is Euclidean distance.
- Given some input point , find the 'th nearest neighbors from the training set.
- Do a majority vote between these nearest neighbor list and classify the input point as the category with highest number of vote.

### Probabilistic interpretation[edit | edit source]

Consider the classification output as a random variable . Define probability of given input and training data is

The output of the classification is

Read more about probabilistic interpretation here: