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| − | ==K-Nearest Neighbour==
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| − | * Recorded Noel class (15/06):
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| − | :* https://drive.google.com/drive/folders/1BaordCV9vw-gxLdJBMbWioX2NW7Ty9Lm
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| − | :* https://drive.google.com/drive/folders/1BaordCV9vw-gxLdJBMbWioX2NW7Ty9Lm
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| − | * StatQuest: https://www.youtube.com/watch?v=HVXime0nQeI
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| − | {| class="wikitable"
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| − | ! colspan="6" style="text-align: left; font-weight: normal" |
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| − | 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]
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| − | 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.
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| − | This is a simple method, but extremely powerful.
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| − | !style="width: 25%"|'''Regression/Classification'''
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| − | !'''Applications'''
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| − | !Strengths
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| − | !Weaknesses
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| − | !Comments
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| − | !Improvements
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| − | KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry. <nowiki>https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/</nowiki>
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| − | * Computer vision applications:
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| − | :* Optical character recognition
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| − | :* Face recognition
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| − | * Recommendation systems
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| − | * Pattern detection in genetic data
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| − | * The algorithm is simple and effective
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| − | * Fast training phase
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| − | * Capable of reflecting complex relationships
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| − | * Unlike many other methods, no assumptions about the distribution of the data are made
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| − | * The method does not produce any model which limits potential insights about the relationship between features
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| − | * Slow classification phase. Requires lots of memory
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| − | * Can not handle nominal feature or missing data without additional pre-processing
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| − | k-NN is ideal for classification tasks where relationships among the attributes and target classes are:
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| − | * numerous
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| − | * complex
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| − | * difficult to interpret and
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| − | * where instances of a class are fairly homogeneous
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| − | :* Weighting training examples based on their distance
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| − | :* Alternative measures of "nearness"
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| − | :* Finding "close" examples in a large training set quickly
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| − | |}
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| − | <br />
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| − | Basic Implementation:
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| − | * Training Algorithm:
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| − | :* Simply store the training examples
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| − | * Prediction Algorithm:
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| − | :# Calculate the distance from x to all points in your data (Udemy Course)
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| − | :# Sort the points in your data by increasing distance from x (Udemy Course)
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| − | :# Predict the majority label of the "k" closets points (Udemy Course)
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| − | :* Find the <math>k</math> training examples <math>(x_{1},y_{1}),...(x_{k},y_{k})</math> that are '''nearest''' to the test example <math>x</math> (Noel)
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| − | :* Predict the most frequent class among those <math>y_{i}'s</math>. (Noel)
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| − | <img src="https://upload.wikimedia.org/wikipedia/commons/e/e7/KnnClassification.svg" class="center" style="display: block; margin-left: auto; margin-right: auto; width: 300pt;" />
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| − | [[File:KNearest_Neighbors_from_the_Udemy_course_Pierian_data1.mp4|800px|thumb|center|Udemy course, Pierian data https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/]]
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