'Classification trees' diaporamas de présentation

Model Building Training

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Predictive modeling with social networks

Predictive modeling with social networks. Jennifer Neville & Foster Provost. Tutorial at ACM SIGKDD 2008. Tutorial AAAI 2008. Social network data is everywhere. Call networks Email networks Movie networks Coauthor networks Affiliation networks Friendship networks

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Introduction on QSAR and modelling of physico-chemical and biological properties

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The Software Infrastructure for Electronic Commerce

The Software Infrastructure for Electronic Commerce. Databases and Data Mining Lecture 4: An Introduction To Data Mining (II) Johannes Gehrke johannes@cs.cornell.edu http://www.cs.cornell.edu/johannes. Lectures Three and Four. Data preprocessing Multidimensional data analysis Data mining

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Section 5

Section 5. Data Mining. Section Content. 5.1 Introduction 5.2 Knowledge Discovery 5.3 Association Rules 5.4 Sequential Patterns 5.5 Classification and Regression 5.6 Other Forms of Data Mining 5. 7 Applications of Data Mining. 5.1 Data Mining Introduction. Data mining:

649 views • 46 slides

Discrimination Methods

Discrimination Methods. As Used In Gene Array Analysis. Discrimination Methods. Microarray Background Clustering and Classifiers Discrimination Methods: Nearest Neighbor Classification Trees Maximum Likelihood Discrimination Fisher Linear Discrimination Aggregating Classifiers Results

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Weka

Weka. Preprocessing. Opening a file. Editing a file. Visualize a variable. Visualizing pairs of variables. Missing values. Discretizing with equalfrequency . Proportional k-interval discretization. FSS: ranking variables with mutual information. FSS. Filter FSS: CFS.

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M Q Hasan Lecturer/ Statistician UN Statistical Institute for Asia and the Pacific Chiba, Japan Email : hasan@unsiap.or.

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Jonathan C-W Chan 1 , Pieter Beckers 2 , Frank Canters 1 ,

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Weka: A Useful Tool for Air Quality Forecasting

Weka: A Useful Tool for Air Quality Forecasting . William F. Ryan Department of Meteorology The Pennsylvania State University wfr1@psu.edu. 2007 National Air Quality Conference, Orlando. Weka. The weka, or woodhen, is a bird native to New Zealand. Weka is

726 views • 27 slides

Near-Minimax Optimal Learning with Decision Trees

Near-Minimax Optimal Learning with Decision Trees. Rob Nowak and Clay Scott. University of Wisconsin-Madison and Rice University. nowak@engr.wisc.edu. Supported by the NSF and the ONR. Basic Problem. Classification : build a decision rule based on labeled training data.

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An adaptive hierarchical questionnaire based on the Index of Learning Styles

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Lecture 10

Tree-based methods, neutral networks. Lecture 10. Tree-based methods. Statistical methods in which the input space (feature space) is partitioned into a set of cuboids (rectangles), and then a simple model is set up in each one. Why decision trees. Compact representation of data

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CART: Classification and Regression Trees

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An Introduction to R

An Introduction to R. Free software for repeatable statistics, visualisation and modeling. Dr Andy Pryke, The Data Mine Ltd Andy@AndyPryke.com. Outline. 1. Overview What is R? When to use R? Wot no GUI? Help and Support. 2. Examples Simple Commands Statistics Graphics

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HLT Specifications

HLT Specifications. Translation Components Nespole! Trento Meeting May 26, 2000. Nespole! HLT Objectives. State of the art (C-STAR II): Broad but limited domain (Travel Planning) Spontaneous spoken language (disfluencies, incomplete and non grammatical sentences)

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Classification PSF Analysis

A New Analysis Tool: Insightful Miner Classification Trees From Cuts Classification Trees: Recasting of the GLAST PSF Analysis Energy Dependencies Present status of GLAST PSFs. Classification PSF Analysis. CPA. A Data Mining Tool. An Miner Analysis Program!.

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- Sachin Singh

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D ATA M INING A N O VERVIEW

D ATA M INING A N O VERVIEW. BY : J OSEPH C ASABONA. Data Warehouse-->. O VERVIEW. What is Data Mining? Introduction to KDD Type of Data found using Data Mining The 4 Goals of Data Mining Case Study: MetLife. W HAT IS D ATA M INING ?.

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Tree methods: Dependent variable is categorical

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