# 2.1.13.1.KNN Theory

## 1.K Nearest Neighbor

* 找和新數據最近的K個鄰居, 這些鄰居是什麼分類, 那麼新數據就是什麼樣的分類 (Choosing a K will affect what class a new point is assigned to)

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-06-24%20下午3.37.55.png)

* Training algorithm
  * Store all the data
* Prediction algorithm
  1. Calculate the distance from x to all points in your data
  2. Sort the points in your data by increasing distance from x
  3. Predict the majority label of the "k" closet points

## 2.Pros and cons

* Pros
  1. Very simple
  2. Training is trivial
  3. Works with any number of classes
  4. Easy to add more data
  5. Few parameters
     * K
     * Distance metric
* Cons
  1. High prediction cost (worse for large data sets)
  2. Not good with high dimensional data
  3. Categorical features do not work well


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