KNN.pdf k nearest neghnor classifier in machine
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The document describes the k-nearest neighbors (k-nn) algorithm for classifying tissue samples as good or bad based on proximity to training samples. It highlights that k-nn is non-parametric and a lazy learner, as it stores the dataset without immediate learning and classifies new data upon request. The algorithm typically uses an odd value for k to avoid ties and relies on majority labels from the nearest samples.
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KNN.pdf k nearest neghnor classifier in machine
- 4. Example for Classification - Whether tissue is good? (3,7) x y Class 7 7 Bad 7 4 Bad 3 4 Good 1 4 Good (7,7) |7-3| + |7-7| = 0 Bad (7,4) |7-3| + |4-7| = 7 Bad (3,4) |3-3| + |4-7| = 3 Good (1,4) |1-3| +|4-7| = 5 Good
- 6. Basic Idea ● k-NN classification rule is to assign to a test sample the majority category label of its k nearest training samples ● In practice, k is usually chosen to be odd, so as to avoid ties ● The k = 1 rule is generally called the nearest-neighbor classification rule
- 8. Steps
- 9. Key points ○ K-NN is a non-parametric algorithm, which means it does not make any assumption on underlying data. ○ It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset. ○ KNN algorithm at the training phase just stores the dataset and when it gets new data, then it classifies that data into a category that is much similar to the new data.
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