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How to represent algorithm using decision tree?

5 years ago in Research Methods By Isiaka Muhammad

Research objective

Hello all! I’m conducting research in machine learning area. When should I consider using decision tree? And how to represent algorithm using decision tree. Someone please explain with an example.

All Answers (3 Answers In All)

By Priyanshu Rathore Answered 5 years ago

You can use the decision tree in the following circumstances. When there is a fixed set of attributes, and these attributes take a small number of disjoint values. When the target functions have discrete results. When there are errors in the training data. When the attribute values are in the training data.

By Fathima M Answered 5 years ago

Consider this example; A ship is sinking and you want to predict if a passenger survives or not. Here we will consider attributes like gender and age. The decision tree will be drawn upside down i.e with its roots at the top. The text in italics represents internal/ decision node. Based on this the tree splits into branches. The branch that doesn’t split is the decision i.e whether the passenger survived not or not which is represented in bold text. YES – Is the passenger a male or a female? – NO ↓ Is age > 10? Survived (24%) â†™ â†˜ Died (72%) is the child > 3? â†™ â†˜ 0.31 Died(2%) survived(2%) 0.69

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