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K-Nearest Neighbour

• 15/06: Recorded class - K-Nearest Neighbour

• https://drive.google.com/drive/folders/1BaordCV9vw-gxLdJBMbWioX2NW7Ty9Lm

• https://drive.google.com/drive/folders/1BaordCV9vw-gxLdJBMbWioX2NW7Ty9Lm

• StatQuest: https://www.youtube.com/watch?v=HVXime0nQeI

KNN is a model that classifies a new data point based on the points that are closest in distance to the new point. The principle behind nearest neighbor methods is to find a predefined number of training samples (K) closest in distance to the new data point. Then, the class of the new data point will be the most common class in the k training samples. https://scikit-learn.org/stable/modules/neighbors.html [Adelo]

In other words, KNN determines the class of a given unlabeled observation by identifying the most common class among the k-nearest labeled observations to it.

This is a simple method, but extremely powerful.

Udemy course, Pierian data https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/

KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry.

k-NN is ideal for classification tasks where relationships among the attributes and target classes are:

• numerous
• complex
• difficult to interpret and
• where instances of a class are fairly homogeneous

Applications of this learning method include:

• Computer vision applications:

• Optical character recognition
• Face recognition

• Recommendation systems
• Pattern detection in genetic data

Basic Implementation:

• Training Algorithm:

• Simply store the training examples

• Prediction Algorithm:

1. Calculate the distance from x to all points in your data (Udemy Course)
2. Sort the points in your data by increasing distance from x (Udemy Course)
3. Predict the majority label of the "k" closets points (Udemy Course)

• Find the $k$ training examples $(x_{1},y_{1}),...(x_{k},y_{k})$ that are nearest to the test example $x$ (Noel)
• Predict the most frequent class among those $y_{i}'s$. (Noel)

• Improvements:

• Weighting training examples based on their distance
• Alternative measures of "nearness"
• Finding "close" examples in a large training set quickly

Strengths and Weaknesses:

Strengths	Weaknesses
The algorithm is simple and effective	The method does not produce any model which limits potential insights about the relationship between features
Fast training phase	Slow classification phase. Requires lots of memory
Capable of reflecting complex relationships	Can not handle nominal feature or missing data without additional pre-processing
Unlike many other methods, no assumptions about the distribution of the data are made

• Classifying a new example: