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83 learningdecisiontree
- 1. Learning Decision Tree Prepared By: Tamboli Tahseen Roll No. : 2140683 Branch: BEIT-SEM (VII) M.H.Saboo Siddik College of Engineering shaikhtahseen6783@gmail.com
- 2. Introduction Decision tree induction is one of the simplest, and yet most successful forms of learning algorithm. It serves as a good introduction to the area of inductive learning, and is easy to implement. A decision tree takes as input an object or situation described by a set of attributes and returns a “decision” the predicted output value for the input. Decision trees represent protocols which can be easily understood by humans.11/26/2016 Intelligent System 2
- 3. Characteristics Root node is a starting node. Decision tree gives the test to be carried out on a decision. Leaf nodes stand for probable final decision. Every node in the tree returns yes/no/probable decision. Branches of the tree are labeled with probable value. Boolean Classification. 11/26/2016 Intelligent System 3
- 5. Attributes Alternate Bar Fri/Sat Hungry Patrons Price Raining Reservation Type WaitEstimate 11/26/2016 Intelligent System 5
- 6. Training Set
- 7. Learning Curve for DT
- 8. Algorithm Function DECISION-TREE-LEARNING(examples, attribs, default)returns a decision tree Inputs: examples, set of examples attribs, set of attribute default, default value for the goal predicate If examples is empty then return default Else if all examples have the same classification then return the classification Else if attribs is empty then return MAJORITY-VALUE(examples) Else best CHOOSE-ATTRIBUTE(attribs, examples) tree a new decision tree with root test best m MAJORITY-VALUE(examples) for each value Vi of best do examples {element of examples with best = Vi} Subtree DECISION-TREE-LEARNING ( examples, attribs --- best, m) add a branch to tree with label Vi and subtree subtree return tree
- 9. Applicability of Decision trees Missing data Multivalued attributes Continuous and integer – valued input attributes Continuous – valued output attributes
- 10. Pros & Cons Pros 1. Are simple to understand and interpret. People are able to understand decision tree models after a brief explanation. 2. Have value even with little hard data. Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and their preferences for outcomes. 3. Help determine worst, best and expected values for different scenarios 4. Can be combined with other decision techniques. Cons 1. For data including categorical variables with different number of levels, information gain in decision trees are biased in favor of those attributes with more levels. 2. Calculations can get very complex particularly if many values are uncertain and/or if many outcomes are linked.
- 11. Thank You 11/26/2016 Intelligent System 11