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Today’s topics

Today’s topics

Today’s topics. Java Implementing Decision Trees Upcoming More formal treatment of grammars Reading Great Ideas , Chapter 2. A decision tree Selecting a textbook. 3. yes. Oh! Pascal by D. Cooper. 1. A programming focus instead of theory. yes. 4. Algorithmics by D. Harel. 0.

By hunter
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Decision Trees

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.

By ban (378 views)

Decision Trees

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.

By sophieanderson (3 views)

Decision Trees

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

By aileen (88 views)

Decision Trees

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

By hue (78 views)

Decision Trees

Decision Trees

Decision Trees. Chapter 08 (part 01) Disclaimer: This PPT is modified based on IOM 530: Intro. to Statistical Learning

By marshr (0 views)

Decision Trees

Decision Trees

Decision Trees. General Learning Task. DEFINE: Set X of Instances (of n- tuples x = < x 1 , ..., x n >) E.g., days decribed by attributes (or features ): Sky, Temp, Humidity, Wind, Water, Forecast Target function y , e.g.:

By moretti (0 views)

Decision Trees

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

By normapuckett (0 views)

Decision Trees

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)

By fala (58 views)

Decision Trees

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.

By terrell (95 views)

Decision Trees

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.

By noelani-price (161 views)