Decision Trees with Variance

Feb 08, 2013

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Your company has developed a new design of mobile phone.You need to decide whether to manufacture and market it inChina or North America.If you manufacture in China, the fixed costs will be $4,000,000, andthe variable costs per phone will be $15.If you manufacture in North America, the fixed

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Decision Trees with Variance

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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.

784 views • 15 slides

Classification with Decision Trees

Classification with Decision Trees. Instructor: Qiang Yang Hong Kong University of Science and Technology Qyang@cs.ust.hk Thanks: Eibe Frank and Jiawei Han. DECISION TREE [Quinlan93]. An internal node represents a test on an attribute.

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Data Mining With Decision Trees

MSCS282 - Data Mining With Decision Trees. 2. Overview. Decision TreesRules and Language BiasConstructing Decision TreesSome AnalysesHeuristicsQuality AssessmentExtensions. MSCS282 - Data Mining With Decision Trees. 3. Goals. Explore the complete data mining processUnderstand decision trees a

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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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Classification with Multiple Decision Trees

Classification with Multiple Decision Trees. CV-2003 Eran Shimonovitz Ayelet Akselrod-Ballin. Plan. Basic framework Query selection, impurity, stopping … Intermediate summary Combining multiple trees Y.Amit & D.Geman’s approach Randomization, Bagging, Boosting Applications.

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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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Predictive Analytics with Decision Trees

Predictive Analytics with Decision Trees. Professors Leo Pipino & Luvai Motiwalla OIS Department, MSB 16.711 (203) Special Topics: Computational Data Modeling (Prof Chandra). Visualization skills to interpret data and present in meaningful ways.

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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 with Numeric Tests

Decision Trees with Numeric Tests. Industrial-strength algorithms. For an algorithm to be useful in a wide range of real-world applications it must: Permit numeric attributes Allow missing values Be robust in the presence of noise

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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.

356 views • 15 slides

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

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

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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