A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails) , each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class.
A decision tree has the substantial advantage of forcing the evaluation of all conceivable outcomes of a decision and tracing each path to a conclusion. It generates a complete analysis of the effects along each branch and flags decision nodes that require additional investigation.
A decision tree makes its decision by running a series of tests.
The ID3 (by Quinlan) algorithm is the fundamental method utilized in decision trees. The ID3 algorithm constructs decision trees in a top-down, greedy manner.
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Explained the concept easily
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Pradeep Kumar Kaushik
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Please give me iris,csv file.
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Ankit Malik
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where is the finaldata.csv
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great learning plateform kushal sir is really too good
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