'Gain ratio' diaporamas de présentation

Data Mining : Implementations

Data Mining : Implementations. Constructing Classifier for B.I. The leading Credit Card Company Visa International and Computer Company Acer Inc. has agreed to market the newly launched Laptop Computer.

3k views • 284 slides

Data Mining : Implementations

Data Mining : Implementations. Constructing Classifier for B.I. The leading Credit Card Company Visa International and Computer Company Acer Inc. has agreed to market the newly launched Laptop Computer.

3.05k views • 284 slides

Data Mining: Classification and Prediction

Data Mining: Classification and Prediction. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation.

7.25k views • 197 slides

Data Mining: Classification and Prediction

Data Mining: Classification and Prediction. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation.

2.6k views • 174 slides

Chapter 6. Classification and Prediction

What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation. Support Vector Machines (SVM) Associative classification

2.36k views • 126 slides

Dr. Yukun Bao School of Management, HUST

Business Data Mining. Dr. Yukun Bao School of Management, HUST. CAN YOU BE A DOCTOR WITHOUT MEDICAL TRAINING?. Production Rules. IF Swollen Glands = Yes THEN Diagnosis = Strep Throat IF Swollen Glands = No & Fever = Yes THEN Diagnosis = Cold IF Swollen Glands = No & Fever = No

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Presentation Covers

541 views • 41 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.

570 views • 41 slides

Fuzzy Interpretation of Discretized Intervals Author: Dr. Xindong Wu IEEE TRANSACTIONS ON FUZZY SYSTEM VOL. 7, NO. 6, DE

Fuzzy Interpretation of Discretized Intervals Author: Dr. Xindong Wu IEEE TRANSACTIONS ON FUZZY SYSTEM VOL. 7, NO. 6, DECEMBER 1999. Presented by: Gong Chen. Outline. Concepts Review Overview Problem Solution Related Techniques Algorithms Design in HCV Experimental Results Conclusions

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Inferring rudimentary rules

Inferring rudimentary rules. 1R: learns a 1-level decision tree I.e., rules that all test one particular attribute Basic version One branch for each value Each branch assigns most frequent class

911 views • 31 slides

Student Grade Prediction Intelligent System

Student Grade Prediction Intelligent System. Under guidance of Dr. Sudip Sanyal IIIT-Allahabad. Student Dinh Ngoc Lan MS200507 M.Tech of Software Engineering IIIT-Allahabad. Content. Objective of the Project C4.5 Algorithm on Student Grade Prediction Intelligent System

1.47k views • 30 slides

Classification: Decision Trees

Classification: Decision Trees. Outline. Top-Down Decision Tree Construction Choosing the Splitting Attribute Information Gain and Gain Ratio. DECISION TREE. An internal node is a test on an attribute. A branch represents an outcome of the test, e.g., Color=red.

455 views • 30 slides

Classification with Decision Trees I

Classification with Decision Trees I. Instructor: Qiang Yang Hong Kong University of Science and Technology Qyang@cs.ust.hk Thanks: Eibe Frank and Jiawei Han. INTRODUCTION. Given a set of pre-classified examples, build a model or classifier to classify new cases.

379 views • 29 slides

Classification: Decision Trees

Classification: Decision Trees. Outline. Top-Down Decision Tree Construction Choosing the Splitting Attribute Information Gain and Gain Ratio. DECISION TREE. An internal node is a test on an attribute. A branch represents an outcome of the test, e.g., Color=red.

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ICS 273A Intro Machine Learning

ICS 273A Intro Machine Learning. decision trees, random forests,. Decision Trees. Problem: decide whether to wait for a table at a restaurant, based on the following attributes : Alternate : is there an alternative restaurant nearby? Bar : is there a comfortable bar area to wait in?

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ICS 273A Intro Machine Learning

ICS 273A Intro Machine Learning. decision trees, random forests,. Decision Trees. Problem: decide whether to wait for a table at a restaurant, based on the following attributes : Alternate : is there an alternative restaurant nearby? Bar : is there a comfortable bar area to wait in?

289 views • 20 slides

Data Mining CSCI 307, Spring 2019 Lecture 16

Data Mining CSCI 307, Spring 2019 Lecture 16. Constructing Trees Covering algorithms. Computing the Gain Ratio. Gain ratio corrects the information gain by taking the intrinsic information of a split into account. Example : intrinsic information for ID code

847 views • 18 slides

Evaluating Hypotheses Reading: Coursepack : Learning From Examples, Section 4 (pp. 16-21)

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Attenuation length and PMT gain ratio study of the TC bars

Attenuation length and PMT gain ratio study of the TC bars. K. Fratini for the TC group INFN-Genova. Outline. The aim of this study is the evaluation of the attenuation length and the PMT gain ratio of the TC bars as a function of the time: two slightly different methods

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Slides for Class 6 and 7 February 14 and 21, 2002

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