Download presentation

Presentation is loading. Please wait.

Published byKierra Gulick Modified over 3 years ago

1
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Classification: Definition l Given a collection of records (training set) l Find a model for class attribute as a function of the values of other attributes. l Goal: previously unseen records should be assigned a class as accurately as possible. –A test set is used to determine the accuracy.

2
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2 Illustrating Classification Task

3
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 3 Examples of Classification Task l Predicting tumor cells as benign or malignant l Classifying credit card transactions as legitimate or fraudulent l Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil l Categorizing news stories as finance, weather, entertainment, sports, etc

4
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 4 Classification Techniques l Decision Tree based Methods l Rule-based Methods l Memory based reasoning l Neural Networks l Naïve Bayes and Bayesian Belief Networks l Support Vector Machines

5
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 5 Example of a Decision Tree categorical continuous class Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Splitting Attributes Training Data Model: Decision Tree

6
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 6 Another Example of Decision Tree categorical continuous class MarSt Refund TaxInc YES NO Yes No Married Single, Divorced < 80K> 80K There could be more than one tree that fits the same data!

7
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 7 Decision Tree Classification Task Decision Tree

8
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 8 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data Start from the root of tree.

9
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 9 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data

10
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 10 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data

11
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 11 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data

12
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 12 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data

13
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 13 Apply Model to Test Data Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Test Data Assign Cheat to “No”

14
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 14 Decision Tree Classification Task Decision Tree

15
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 15 Decision Tree Induction l Many Algorithms: –Hunt’s Algorithm (one of the earliest) –CART –ID3, C4.5 –SLIQ,SPRINT

16
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 16 General Structure of Hunt’s Algorithm l D t = set of training records of node t l General Procedure: –If D t only records of same class y t t leaf node labeled as y t –Else: use an attribute test to split the data. l Recursively apply the procedure to each subset. DtDt ?

17
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 17 Hunt’s Algorithm Don’t Cheat Refund Don’t Cheat Don’t Cheat YesNo Refund Don’t Cheat YesNo Marital Status Don’t Cheat Single, Divorced Married Taxable Income Don’t Cheat < 80K>= 80K Refund Don’t Cheat YesNo Marital Status Don’t Cheat Single, Divorced Married

18
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 18 Tree Induction l Greedy strategy. –Split the records based on an attribute test that optimizes a local criterion. l Issues –Determine how to split the records How to specify the attribute test condition? How to determine the best split? –Determine when to stop splitting

19
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 19 Tree Induction l Greedy strategy. –Split the records based on an attribute test that optimizes certain criterion. l Issues –Determine how to split the records How to specify the attribute test condition? How to determine the best split? –Determine when to stop splitting

20
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 20 How to Specify Test Condition? l Depends on attribute types –Nominal(No order; e.g., Country) –Ordinal(Discrete, order; e.g., S,M,L,XL) –Continuous (Ordered, cont.; e.g., temperature) l Depends on number of ways to split –2-way split –Multi-way split

21
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 21 Splitting Based on Nominal Attributes l Multi-way split: Use as many partitions as distinct values. l Binary split: Divides values into two subsets. Need to find optimal partitioning. CarType Family Sports Luxury CarType {Family, Luxury} {Sports} CarType {Sports, Luxury} {Family} OR

22
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 22 Splitting Based on Continuous Attributes l Different ways of handling –Discretization to form an ordinal categorical attribute Static – discretize once at the beginning Dynamic – ranges can be found by equal interval bucketing, equal frequency bucketing (percentiles), or clustering. –Binary Decision: (A < v) or (A v) consider all possible splits and finds the best cut can be more compute intensive

23
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 23 Splitting Based on Continuous Attributes

24
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 24 Tree Induction l Greedy strategy. –Split the records based on an attribute test that optimizes certain criterion. l Issues –Determine how to split the records How to specify the attribute test condition? How to determine the best split? –Determine when to stop splitting

25
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 25 How to determine the Best Split Before Splitting: 10 records of class 0, 10 records of class 1 Which test condition is the best?

26
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 26 How to determine the Best Split l Greedy approach: –Nodes with homogeneous class distribution are preferred l Need a measure of node impurity: Non-homogeneous, High degree of impurity Homogeneous, Low degree of impurity

27
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 27 Measures of Node Impurity l Gini Index l Entropy l Misclassification error

28
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 28 How to Find the Best Split B? YesNo Node N3Node N4 A? YesNo Node N1Node N2 Before Splitting: M0 M1 M2M3M4 M12 M34 Gain = M0 – M12 vs M0 – M34

29
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 29 Measure of Impurity: GINI l Gini Index for a given node t : (NOTE: p( j | t) is the relative frequency of class j at node t). –Maximum (1 - 1/n c ) when records are equally distributed among all classes, implying least interesting information –Minimum (0.0) when all records belong to one class, implying most interesting information

30
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 30 Examples for computing GINI P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Gini = 1 – P(C1) 2 – P(C2) 2 = 1 – 0 – 1 = 0 P(C1) = 1/6 P(C2) = 5/6 Gini = 1 – (1/6) 2 – (5/6) 2 = 0.278 P(C1) = 2/6 P(C2) = 4/6 Gini = 1 – (2/6) 2 – (4/6) 2 = 0.444

31
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 31 Splitting Based on GINI l Used in CART, SLIQ, SPRINT. l When a node p is split into k partitions (children), the quality of split is computed as, where,n i = number of records at child i, n = number of records at node p.

32
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 32 Binary Attributes: Computing GINI Index l Splits into two partitions l Effect of Weighing partitions: –Larger and Purer Partitions are sought for. B? YesNo Node N1Node N2 Gini(N1) = 1 – (5/6) 2 – (2/6) 2 = 0.194 Gini(N2) = 1 – (1/6) 2 – (4/6) 2 = 0.528 Gini(Children) = 7/12 * 0.194 + 5/12 * 0.528 = 0.333

33
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 33 Tree Induction l Greedy strategy. –Split the records based on an attribute test that optimizes certain criterion. l Issues –Determine how to split the records How to specify the attribute test condition? How to determine the best split? –Determine when to stop splitting

34
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 34 Stopping Criteria for Tree Induction l Stop expanding a node when all the records belong to the same class l Stop expanding a node when all the records have similar attribute values l Early termination (to be discussed later)

35
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 35 Decision Tree Based Classification l Advantages: –Inexpensive to construct –Extremely fast at classifying unknown records –Easy to interpret for small-sized trees –Accuracy is comparable to other classification techniques for many simple data sets

36
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 36 Practical Issues of Classification l Underfitting and Overfitting l Missing Values l Costs of Classification

37
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 37 Underfitting and Overfitting Overfitting Underfitting: when model is too simple, both training and test errors are large Underfitting

38
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 38 Overfitting due to Noise Decision boundary is distorted by noise point

39
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 39 Overfitting due to Insufficient Examples Lack of data points in the lower half of the diagram makes it difficult to predict correctly the class labels of that region - Insufficient number of training records in the region causes the decision tree to predict the test examples using other training records that are irrelevant to the classification task

40
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 40 Notes on Overfitting l Overfitting results in decision trees that are more complex than necessary l Training error no longer provides a good estimate of how well the tree will perform on previously unseen records l Need new ways for estimating errors

41
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 41 How to Address Overfitting l Pre-Pruning (Early Stopping Rule) –Stop the algorithm before it becomes a fully- grown tree Stop if number of instances is less than some user- specified threshold Stop if class distribution of instances are independent of the available features (e.g., using 2 test) Stop if expanding the current node does not improve impurity measures (e.g., Gini or information gain).

42
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 42 How to Address Overfitting… l Post-pruning –Grow decision tree to its entirety –Trim the nodes of the decision tree in a bottom-up fashion –If generalization error improves after trimming, replace sub-tree by a leaf node. –Class label of leaf node is determined from majority class of instances in the sub-tree

43
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 43 Model Evaluation l Metrics for Performance Evaluation –How to evaluate the performance of a model? l Methods for Performance Evaluation –How to obtain reliable estimates? l Methods for Model Comparison –How to compare the relative performance among competing models?

44
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 44 Metrics for Performance Evaluation l Focus on the predictive capability of a model –Rather than how fast it takes to classify or build models, scalability, etc. l Confusion Matrix: PREDICTED CLASS ACTUAL CLASS Class=YesClass=No Class=Yesab Class=Nocd a: TP (true positive) b: FN (false negative) c: FP (false positive) d: TN (true negative)

45
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 45 Metrics for Performance Evaluation… l Most widely-used metric: PREDICTED CLASS ACTUAL CLASS Class=YesClass=No Class=Yesa (TP) b (FN) Class=Noc (FP) d (TN)

46
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 46 Limitation of Accuracy l Consider a 2-class problem –Number of Class 0 examples = 9990 –Number of Class 1 examples = 10 l If model predicts everything to be class 0, accuracy is 9990/10000 = 99.9 % –Accuracy is misleading because model does not detect any class 1 example

47
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 47 Cost-Sensitive Measures l Precision is biased towards C(Yes|Yes) & C(Yes|No) l Recall is biased towards C(Yes|Yes) & C(No|Yes) l F-measure is biased towards all except C(No|No)

Similar presentations

OK

Classification Basic Concepts, Decision Trees, and Model Evaluation

Classification Basic Concepts, Decision Trees, and Model Evaluation

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google