Download presentation

Presentation is loading. Please wait.

Published byAnne Hollyfield Modified over 2 years ago

1
Bab 4.1 - 1/44 Bab 4 Classification: Basic Concepts, Decision Trees & Model Evaluation Part 1 Classification With Decision tree

2
Bab 4.1 - 2/44 Classification: Definition

3
Bab 4.1 - 3/44 Example of Classification Task

4
Bab 4.1 - 4/44 General Approach for Building Classification Model

5
Bab 4.1 - 5/44 Classification Techniques

6
Bab 4.1 - 6/44 Example of Decision Tree

7
Bab 4.1 - 7/44 Another Example of Decision Tree

8
Bab 4.1 - 8/44 Decision Tree Classification Task

9
Bab 4.1 - 9/44 Apply Model to Test Data

10
Bab 4.1 - 10/44 Decision Tree Classification Task

11
Bab 4.1 - 11/44 Decision Tree Induction

12
Bab 4.1 - 12/44 General Structure of Hunt’s Algorithm

13
Bab 4.1 - 13/44 Hunt’s Algorithm

14
Bab 4.1 - 14/44 Design Issues of Decision Tree Induction

15
Bab 4.1 - 15/44 Methods for Expression Test Conditions

16
Bab 4.1 - 16/44 Test Condition for Nominal Attributes

17
Bab 4.1 - 17/44 Test Condition for Ordinal Attributes

18
Bab 4.1 - 18/44 Test Condition for Continues Attributes

19
Bab 4.1 - 19/44 Splitting Based on Continues Attributes

20
Bab 4.1 - 20/44 How to Determine the Best Split / 1

21
Bab 4.1 - 21/44 How to Determine the Best Split / 2

22
Bab 4.1 - 22/44 Measures of Node Impurity

23
Bab 4.1 - 23/44 Finding the Best Split / 1

24
Bab 4.1 - 24/44 Finding the Best Split / 2

25
Bab 4.1 - 25/44 Measure of Impurity: GINI

26
Bab 4.1 - 26/44 Computing GINI Index of a Single Node

27
Bab 4.1 - 27/44 Computing GINI Index for a Collection of Nodes

28
Bab 4.1 - 28/44 Binary Attributes: Computing GINI Index

29
Bab 4.1 - 29/44 Categorical Attributes: Computing GINI Index

30
Bab 4.1 - 30/44 Continuous Attributes: Computing GINI Index / 1

31
Bab 4.1 - 31/44 Continuous Attributes: Computing GINI Index / 2

32
Bab 4.1 - 32/44 Measure of Impurity: Entropy

33
Bab 4.1 - 33/44 Computing Entropy of a Single Node

34
Bab 4.1 - 34/44 Computing information Gain After Splitting

35
Bab 4.1 - 35/44 Problems with Information Gain

36
Bab 4.1 - 36/44 Gain Ratio

37
Bab 4.1 - 37/44 Measure of Impurity: Classification Error

38
Bab 4.1 - 38/44 Computing Error of a Single Node

39
Bab 4.1 - 39/44 Comparison among Impurity Measures For binary (2-class) classification problems

40
Bab 4.1 - 40/44 Misclassification Error vs Gini index

41
Bab 4.1 - 41/44 Example: C4.5 Simple depth-first construction. Uses Information Gain Sorts Continuous Attributes at each node. Needs entire data to fit in memory. Unsuitable for Large Datasets. Needs out-of-core sorting. You can download the software from: http://www.cse.unsw.edu.au/~quinlan/c4.5r8.tar.gz http://www.cse.unsw.edu.au/~quinlan/c4.5r8.tar.gz

42
Bab 4.1 - 42/44 Scalable Decision Tree Induction / 1 How scalable is decision tree induction? Particularly suitable for small data set SLIQ (EDBT’96 — Mehta et al.) Builds an index for each attribute and only class list and the current attribute list reside in memory

43
Bab 4.1 - 43/44 Scalable Decision Tree Induction / 2 SLIQ Sample data for the class buys_computer Disk-resident attribute lists Memory-resident class list RIDCredit_ratingAgeBuys_computer 1excellent38yes 2excellent26yes 3fair35no 4excellent49no Credit_ratingRID excellent1 2 4 fair3 …… ageRID 262 353 381 494 …… RIDBuys_computernode 1yes5 2 2 3no3 4 6 ……… 0 12 3 4 5 6

44
Bab 4.1 - 44/44 Decision Tree Based Classification Advantages Inexpensive to construct Extremely fast at classifying unknown records Easy to interpret for small-sized tress Accuracy is comparable to other classification techniques for many data sets Practical Issues of Classification Underfitting and Overfitting Missing Values Costs of Classification

Similar presentations

OK

Lecture 7. Outline 1. Overview of Classification and Decision Tree 2. Algorithm to build Decision Tree 3. Formula to measure information 4. Weka, data.

Lecture 7. Outline 1. Overview of Classification and Decision Tree 2. Algorithm to build Decision Tree 3. Formula to measure information 4. Weka, data.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on writing book reviews Ppt on knowledge management Ppt on chromosomes Ppt on fast food in india Ppt on rc coupled amplifier Numeric display ppt on tv Ppt on consumer protection act 1986 india Ppt on nutrition in animals class 7 Ppt on water softening techniques dance Download ppt on trigonometric functions for class 11