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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 determines the class of a given unlabeled observation by identifying the k-nearest labeled observations to it. In other words, the algorithm assigns a given unlabeled observation to the class that has more similar labeled instances. This is a simple method, but very powerful.

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

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 Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle k} training examples Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle (x_{1},y_{1}),...(x_{k},y_{k})} that are nearest to the test example Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle x} (Noel)
• Predict the most frequent class among those Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 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: