Decision Trees

Decision Trees. Prof. A.L. Yuille. Stat 231. Fall 2004. Duda, Hart & Stork. Chp 81-8.3 . Decision Trees. Decision Trees. Binary Classification Trees. Data can be non-metric, list of binary attributes. 2. Classification Decision Trees. Nonmetric. Data is list of binary attributes.

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Decision Trees

Decision Trees. Example: Conducted survey to see what customers were interested in new model car Want to select customers for advertising campaign. training set. Basic Information Gain Computations. Result: I_Gain_Ratio: city>age>car. Result: I_Gain: age > car=city.

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Decision Trees

Decision Trees. Daisy Guevara Gilberto Jiménez Orlando Ruiz Jan Salomón. What is a rooted tree?. It is a tree in which one vertex has been designated as the root and every edge is directed away from the root. What are decision trees?.

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Decision Trees

Decision Trees. Highly used and successful Iteratively split the Data Set into subsets one attribute at a time, using most informative attributes first Continue until you can label each leaf node with a class Attribute Features – discrete/nominal (can extend to continuous features)

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Decision Trees

Decision Trees. Example. Example 2. Examples, which one is better?. Good when. Samples are attribute-value pairs Target function has discrete output values Disjunctions required Missing, noisy training data. Construction. Top down construction

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Decision Trees

Decision Trees. Shalev Ben-David. Definition. Given a function and oracle access to , determine f(x) with minimum number of queries E.g. f is OR on the bits of x – Grover search D(f) is the deterministic query complexity R(f) is the randomized query complexity

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Decision Trees

Decision Trees. Many classes of problems can be formalized as search problems in decision trees . We are going to look at two different applications of search trees: First, we will solve the n-queens problem using backtracking search.

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Decision Trees

Decision Trees. Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting. Another Example Problem. A Decision Tree. Type. Car. SUV. Minivan. -. Doors. Tires. 2. 4. Blackwall. Whitewall. +. -. -. +. Decision Trees. Decision tree representation

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Decision Trees

Decision Trees. Prof. A.L. Yuille. Stat 231. Fall 2004. Duda, Hart & Stork. Chp 81-8.3. Decision Trees. Decision Trees. Binary Classification Trees. Data can be non-metric, list of binary attributes. 2. Classification Decision Trees. Nonmetric. Data is list of binary attributes.

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Decision Trees

Decision Trees. Greg Grudic (Notes borrowed from Thomas G. Dietterich and Tom Mitchell) Modified by Longin Jan Latecki Some slides by Piyush Rai. Outline. Decision Tree Representations ID3 and C4.5 learning algorithms (Quinlan 1986) CART learning algorithm (Breiman et al. 1985)

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Decision Trees

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Decision Trees

Decision Trees. a n introduction. Entropy over the class attribute. A class attribute contains uncertainty over the values uncertainty captured by entropy H( p ) of the target Increase certainty about the class by considering other attributes

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DECISION TREES

DECISION TREES. An internal node represents a test on an attribute. A branch represents an outcome of the test, e.g., Color=red. A leaf node represents a class label or class label distribution.

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Decision Trees

Decision Trees. The “No Free Lunch” Theorem. Is there any representation that is compact (ie, sub-exponential in n) for all functions? Function = truth table n attributes  2^n rows in table Classification/target column is 2^n long

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Decision Trees

Decision Trees. Greg Grudic (Notes borrowed from Thomas G. Dietterich and Tom Mitchell) [Edited by J. Wiebe]. Outline. Decision Tree Representations ID3 and C4.5 learning algorithms (Quinlan 1986) CART learning algorithm (Breiman et al. 1985) Entropy, Information Gain Overfitting.

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Decision trees

Decision trees. Decision trees. Originally invented for concept learning (two-class classification) over discrete features But later it was extended to more classes And also to continuous features (here: “attributes”) Also closely related to the learning of logical formulas

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Decision Trees

Decision Trees. Jianping Fan Dept of Computer Science UNC-Charlotte. The problem of Classification. Given a set of training samples and their attribute values (x) and labels (y), try to determine the labels y of new examples. Classifier Training y = f(x) Prediction y given x.

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Decision Trees

color. color. green. green. red. red. blue. blue. shape. shape. pos. C. neg. B. circle. circle. triangle. triangle. square. square. B. neg. C. neg. pos. A. Decision Trees.

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Decision Trees

Decision Trees. categorical. categorical. continuous. class. Example of a Decision Tree. Splitting Attributes. Refund. Yes. No. NO. MarSt. Married. Single, Divorced. TaxInc. NO. < 80K. > 80K. YES. NO. Model: Decision Tree. Training Data. NO. Another Example of Decision Tree.

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