Solving Influence Diagrams Plus more Decision Trees

Oct 02, 2012

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Solving Influence Diagrams Plus more Decision Trees

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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.

826 views • 15 slides

Decision Diagrams

Decision Diagrams. Maciej Ciesielski Electrical & Computer Engineering University of Massachusetts, Amherst, USA ciesiel@ecs.umass.edu. Common Representations. Boolean functions ( f : B  B ) Truth table, Karnaugh map SoP, PoS, ESoP Reed-Muller expansions (PPRM, FPRM, GRM, etc.)

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Influence Diagrams Basic Decision Trees

Graphic symbols . NodesSquare = decisionsCircles = chance eventsRectangles with rounded corners = valuesArrows (arcs) are used to connect nodesA node at the beginning of an arc is called a predecessorA node at the end of an arc is a called a successor . . . . . Venture Capital Decision. Value.

422 views • 16 slides

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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Influence Diagrams, Decision Trees and Probability

Influence Diagrams. Another way to structure decision problems Graphical representation Considers: Decisions to make Uncertain eventsValue of outcomes. }. Nodes in network. . Chance Node. . Decision Node. . Value Node.

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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 and more!

Decision Trees and more!. Learning OR with few attributes. Target function: OR of k literals Goal: learn in time polynomial in k and log n e and d constants ELIM makes “slow” progress might disqualifies only one literal per round Might remain with O(n) candidate literals.

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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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Solving Influence Diagrams

Solving Influence Diagrams. Influence Diagrams are usually solved using software Freeware and Commercial software exists For the exam, follow procedure used in today’s lecture. Simple tricks for Solving Influence Diagrams.

366 views • 15 slides

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.

380 views • 15 slides

Chapter 6 Decision Trees and Influence Diagrams

Chapter 6 Decision Trees and Influence Diagrams. Introduction. Decision problems are multi-stage in character when the choice of a given option may result in circumstances which will require yet another decision to be made.

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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. 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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Constructing influence diagrams

Constructing influence diagrams. Decision-maker: EPA Decision: Permit use of potentially carcinogenic chemical? Alternative courses of action Permit Restrict Ban Uncertainty: hazards Imperfect information: test and survey. Consequences. Value. Decision. Usage decision. Economic

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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.

1.05k views • 94 slides

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.

450 views • 38 slides

Influence Diagrams, Decision Trees and Probability

Influence Diagrams, Decision Trees and Probability. Reference: Clemen & Reilly. Making Hard Decisions , 2nd ed. Chapter 3. Duxbury, 2001. NOTE: Some materials for this presentation courtesy of Dr. Dan Maxwell. Influence Diagrams. Another way to structure decision problems

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