Description: steps:
In order to determine the unknown instance categories, with examples of all known categories as reference
Parameter selection of K
The calculation examples and all known examples of the unknown distance
Choose the closest known instance K
According to the majority voting rule (majority-voting), for example the unknown classified as K most adjacent to the majority in the sample category
To Search:
File list (Check if you may need any files):
01DTree\3.1 决策树(decision tree)算法.html
01DTree\3.1 决策树(decision tree)算法_files\c2cec3fdfc0392456a6ac4258694a4c27d1e2538.jpg
01DTree\3.1 决策树(decision tree)算法_files\Image [1].png
01DTree\3.1 决策树(decision tree)算法_files\Image [2].png
01DTree\3.1 决策树(decision tree)算法_files\Image [3].png
01DTree\3.1 决策树(decision tree)算法_files\Image [4].png
01DTree\3.1 决策树(decision tree)算法_files\Image [5].png
01DTree\3.1 决策树(decision tree)算法_files\Image [6].png
01DTree\3.1 决策树(decision tree)算法_files\Image [7].png
01DTree\3.1 决策树(decision tree)算法_files\Image [8].png
01DTree\3.1 决策树(decision tree)算法_files\Image.png
01DTree\3.1 决策树(decision tree)算法_files\Thumbs.db
01DTree\3.2 决策树(decision tree)应用.html
01DTree\3.2 决策树(decision tree)应用_files\Image.png
01DTree\3.2 决策树(decision tree)应用_files\Thumbs.db
01DTree\allElectronicInformationGainOri.dot
01DTree\AllElectronics.csv
01DTree\AllElectronics.py
01DTree\3.1 决策树(decision tree)算法_files
01DTree\3.2 决策树(decision tree)应用_files
01DTree
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