Decision tree

Decision tree LING 572 Fei Xia 1/16/06 Outline Basic concepts Issues  In this lecture, “attribute” and “feature” are interchangeable. Basic concepts Main idea Build a tree  decision tree Each node represents a test Training instances are split at each node Greedy algorithm

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Decision Tree Models in Data Mining

Decision Tree Models in Data Mining. Matthew J. Liberatore Thomas Coghlan. Decision Trees in Data Mining . Decision Trees can be used to predict a categorical or a continuous target (called regression trees in the latter case)

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Data Mining, Decision Trees and Earthquake Prediction

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Data Mining and Decision Trees

Lecture 2 CS157B. Data Mining and Decision Trees. Prof. Sin-Min Lee Department of Computer Science. Evolution of Database Technology. 1960s: Data collection, database creation, IMS and network DBMS 1970s: Relational data model, relational DBMS implementation 1980s:

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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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Data Mining using Decision Trees

Decision Trees from Data Base. ExAttAttAttConceptNumSizeColourShapeSatisfied1medbluebrickyes2smallredwedgeno3smallredsphereyes4largeredwedgeno5largegreenpillaryes6largeredpillarno7largegreensphereyes. . Choose target : Concept s

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Decision tree

Decision tree. LING 572 Fei Xia 1/16/06. Outline. Basic concepts Issues  In this lecture, “attribute” and “feature” are interchangeable. Basic concepts. Main idea. Build a tree  decision tree Each node represents a test Training instances are split at each node Greedy algorithm.

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DATA MINING: DEFINITIONS AND DECISION TREE EXAMPLES

DATA MINING: DEFINITIONS AND DECISION TREE EXAMPLES. Emily Thomas Director of Planning and Institutional Research. WHAT IS DATA MINING?. Data mining is the discovery of hidden knowledge, unexpected patterns and new rules in large databases.

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Data Mining for Decision Making

DSI Annual Meeting Baltimore November 16, 2013. Data Mining for Decision Making. Mary Malliaris Loyola University Chicago. What is Data Mining?.

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Data Warehouses, Decision Support and Data Mining

Data Warehouses, Decision Support and Data Mining. University of California, Berkeley School of Information IS 257: Database Management. Review Data Warehouses (Based on lecture notes from Joachim Hammer, University of Florida, and Joe Hellerstein and Mike Stonebraker of UCB)

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Decision Support: Data Mining

Decision Support: Data Mining. Introduction. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful

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DECISION TREE

DECISION TREE. Developed for the National Petroleum Technology Office By Brian Keltch & Andrew Hastings bkeltch@npto.doe.gov (918) 699-2065 TRW. Presented By Len Volk. What Is It?. Computer program Aids making decisions & risks in these decisions Organizes available information

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Data Mining and Decision Support Integration

ACAI’05/SEKT’05 ADVANCED COURSE ON KNOWLEDGE DISCOVERY. Data Mining and Decision Support Integration. Marko Bohanec Jo žef Stefan Institut e Department of Knowledge Technologies & University of Ljubljana Faculty of Administration. Decision Support. modeling. model. decision makers+

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Data Mining, Decision Trees and Earthquake Prediction

CS157B Lecture 4. Data Mining, Decision Trees and Earthquake Prediction. Professor Sin-Min Lee. What is Data Mining?. Process of automatically finding the relationships and patterns, and extracting the meaning of enormous amount of data. Also called “knowledge discovery”. Objective.

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Data Warehouses, Decision Support and Data Mining

Data Warehouses, Decision Support and Data Mining. University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management. Review. Data Warehousing. Problem: Heterogeneous Information Sources. “Heterogeneities are everywhere”. Personal Databases.

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Data Mining using Decision Trees

Data Mining using Decision Trees. Professor J. F. Baldwin. Decision Trees from Data Base. Ex Att Att Att Concept Num Size Colour Shape Satisfied 1 med blue brick yes 2 small red wedge no 3 small red sphere yes 4 large red wedge no 5 large green pillar yes

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DECISION TREE

DECISION TREE. Project Risk Management: A combined Analytic Hierarchy Process and Decision Tree Approach Dr.Prasanta Kumar Dey. CONTENT. What is the decision tree? The example of decision tree The advantage of decision tree The application of decision tree. WHAT IS THE DECISION TREE?.

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Decision Tree

The decision tree is the classification algorithm in ML(machine learning). A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. To understand the algorithm of the decision tree we need to know about the classification. https://www.learnbay.co/data-science-course/blog-post/decision-tree/

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Data Mining and Decision Support

Data Mining and Decision Support. Data Warehousing: create/ select target database. Sampling: choose data for building models. Data Cleaning: supply missing values eliminate noisy data. Data Mining: choose data mining tasks choose data mining methods to extract patterns / knowledge.

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Decision Tree

Decision Tree. Developed by: Dr Eddie Ip Modified by: Dr Arif Ansari. Outline. Example Concept. Decision Tree: Example 1. Life insurance: whether preferred rate should be given? ID low risk group & give preferred rate Criteria: smoking ? overweight ?. Decision Tree: Example 2.

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Decision support, data mining & data warehousing

Decision support, data mining & data warehousing. Decision-support systems are used to make business decisions often based on data collected by on-line transaction-processing systems. Examples of business decisions: What items to stock? What insurance premium to change?

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Decision Tree

Decision Tree. Classification. Databases are rich with hidden information that can be used for making intelligent decisions. Classification is a form of data analysis that can be used to extract models describing important data classes. Data classification process. Learning

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