Machine Learning with Weka

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Machine Learning with Weka"

Transcription

1 Machine Learning with Weka SLIDES BY (TOTAL 5 Session of 1.5 Hours Each) ANJALI GOYAL & ASHISH SUREKA ( CS 309 INFORMATION RETRIEVAL COURSE ASHOKA UNIVERSITY NOTE: Slides created and edited using existing teaching resources on Internet

2 WEKA: the software Machine learning/data mining software written in Java (distributed under the GNU Public License) Used for research, education, and applications Main features: Comprehensive set of data pre-processing tools, learning algorithms and evaluation methods Graphical user interfaces (incl. data visualization) Environment for comparing learning algorithms 2

3 WEKA: download and install Go to website: 3

4 WEKA: download and install Go to website: 4

5 WEKA only deals with flat age sex { female, chest_pain_type { typ_angina, asympt, non_anginal, cholesterol exercise_induced_angina { no, class { present, 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present... 5

6 WEKA only deals with flat age sex { female, chest_pain_type { typ_angina, asympt, non_anginal, cholesterol exercise_induced_angina { no, class {present, 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present... 6

7 7

8 Explorer: pre-processing the data Data can be imported from a file in various formats: ARFF, CSV Data can also be read from a URL or from an SQL database (using JDBC) Pre-processing tools in WEKA are called filters WEKA contains filters for: Discretization, normalization, resampling, attribute selection, transforming and combining attributes, 8

9 12/27/2017 University of Waikato 9

10 12/27/2017 University of Waikato 10

11 Iris Dataset 11

12 Iris Dataset 12

13 Iris Dataset- Arff 13

14 Distinct is no. of distinct values i.e. total no. of instances if you removed all duplicates. Unique is no. of values that appear only once. What do you observe from this graph? ? Colors? 5, 6,? What do they add to? Is sepallength a good predictor? 12/27/2017 University of Waikato 14

15 Check if sepalwidth is good predictor? 12/27/2017 University of Waikato 15

16 12/27/2017 University of Waikato 16

17 12/27/2017 University of Waikato 17

18 12/27/2017 University of Waikato 18

19 Which of the 4 attributes is better predictor? 12/27/2017 University of Waikato 19

20 Data Processing 12/27/2017 University of Waikato 20

21 Discretization Discretization is the process of putting values into buckets so that there are a limited number of possible states. (continuous to categorical ) Many classification algorithms produce better results on discretized data. 21

22 22

23 23

24 24

25 12/27/2017 University of Waikato 25

26 12/27/2017 University of Waikato 26

27 12/27/2017 University of Waikato 27

28 12/27/2017 University of Waikato 28

29 12/27/2017 University of Waikato 29

30 12/27/2017 University of Waikato 30

31 12/27/2017 University of Waikato 31

32 12/27/2017 University of Waikato 32

33 12/27/2017 University of Waikato 33

34 12/27/2017 University of Waikato 34

35 What should be the best no. of bins? 12/27/2017 University of Waikato 35

36 Explorer: data visualization Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem WEKA can visualize single attributes (1-d) and pairs of attributes (2-d) To do: rotating 3-d visualizations (Xgobi-style) Color-coded class values Jitter option to deal with nominal attributes (and to detect hidden data points) Zoom-in function 36

37 12/27/2017 University of Waikato 37

38 Which two attributes are linearly correlated? 12/27/2017 University of Waikato 38

39 12/27/2017 University of Waikato 39

40 12/27/2017 University of Waikato 40

41 12/27/2017 University of Waikato 41

42 12/27/2017 University of Waikato 42

43 12/27/2017 University of Waikato 43

44 12/27/2017 University of Waikato 44

45 12/27/2017 University of Waikato 45

46 12/27/2017 University of Waikato 46

47 Explorer: attribute selection Panel that can be used to investigate which (subsets of) attributes are the most predictive ones Attribute selection methods contain two parts: A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking An evaluation method: correlation-based, wrapper, information gain, chi-squared, Very flexible: WEKA allows (almost) arbitrary combinations of these two 47

48 12/27/2017 University of Waikato 48

49 12/27/2017 University of Waikato 49

50 12/27/2017 University of Waikato 50

51 12/27/2017 University of Waikato 51

52 12/27/2017 University of Waikato 52

53 12/27/2017 University of Waikato 53

54 12/27/2017 University of Waikato 54

55 12/27/2017 University of Waikato 55

56 12/27/2017 University of Waikato 56

57 Add a new feature to existing dataset such that new feature is most beneficial? Add a feature which has distinct values for all classes. Add a new feature to existing dataset such that new feature is least beneficial? Add a feature which has same values for all classes. 57

58 Lets try with Iris dataset! 12/27/2017 University of Waikato 58

59 12/27/2017 University of Waikato 59

60 12/27/2017 University of Waikato 60

61 12/27/2017 University of Waikato 61

62 Explorer: building classifiers Classifiers in WEKA are models for predicting nominal or numeric quantities Implemented learning schemes include: Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes nets, Meta -classifiers include: Bagging, boosting, stacking, error-correcting output codes, locally weighted learning, 62

63 12/27/2017 University of Waikato 63

64 12/27/2017 University of Waikato 64

65 12/27/2017 University of Waikato 65

66 12/27/2017 University of Waikato 66

67 12/27/2017 University of Waikato 67

68 12/27/2017 University of Waikato 68

69 12/27/2017 University of Waikato 69

70 12/27/2017 University of Waikato 70

71 12/27/2017 University of Waikato 71

72 12/27/2017 University of Waikato 72

73 12/27/2017 University of Waikato 73

74 12/27/2017 University of Waikato 74

75 Training data is again used for testing model. Training data is used for model development and an unseen set of data is used for testing model. It is held one out scheme. Train on a certain percentage of data and then test on rest of data. 12/27/2017 University of Waikato 75

76 12/27/2017 University of Waikato 76

77 Cross Validation Cross Validation is the method for estimating the accuracy of an inducer by dividing the data into K mutually exclusive subsets (folds) of approximately equal size. Simplest and most widely used method for estimating prediction error. 77

78 We use Cross Validation as follows: Divide data into K folds; hold-out one part and fit using the remaining data (compute error rate on hold-out data); repeat K times. CV Error Rate: average over the K errors we have computed. (Let us suppose, K = 5) Original Data Testing Data Training Data K=1 K=2 K=3 K=4 K=5

79 How many folds needed (k=?) Large K: small bias, large variance as well as high computational time. Small K: Computational time reduced, small variance, large bias. A common choice for K is

80 12/27/2017 University of Waikato 80

81 12/27/2017 University of Waikato 81

82 12/27/2017 University of Waikato 82

83 12/27/2017 University of Waikato 83

84 12/27/2017 University of Waikato 84

85 12/27/2017 University of Waikato 85

86 12/27/2017 University of Waikato 86

87 12/27/2017 University of Waikato 87

88 12/27/2017 University of Waikato 88

89 tp fn fp tn 12/27/2017 University of Waikato 89

90 tn fp fn tp 12/27/2017 University of Waikato 90

91 12/27/2017 University of Waikato 91

92 12/27/2017 University of Waikato 92

93 Add a new feature to existing dataset such that new feature is most beneficial? Add a feature which has distinct values for all classes. Add a new feature to existing dataset such that new feature is least beneficial? Add a feature which has same values for all classes. 93

94 Add a new feature to existing dataset such that new feature is most beneficial? Add a feature which has distinct values for all classes. Add a new feature to existing dataset such that new feature is least beneficial? Add a feature which has same values for all classes. 94

95 Lets try with Iris dataset! 12/27/2017 University of Waikato 95

96 12/27/2017 University of Waikato 96

97 12/27/2017 University of Waikato 97

98 12/27/2017 University of Waikato 98

99 12/27/2017 University of Waikato 99

100 12/27/2017 University of Waikato 100

101 12/27/2017 University of Waikato 101

102 12/27/2017 University of Waikato 102

103 12/27/2017 University of Waikato 103

104 Attribute Selection+ Classification (Weather.arff) 104

105 12/27/2017 University of Waikato 105

106 12/27/2017 University of Waikato 106

107 12/27/2017 University of Waikato 107

108 12/27/2017 University of Waikato 108

109 Discretization Discretization is the process of putting values into buckets so that there are a limited number of possible states. (continuous to categorical ) Many classification algorithms produce better results on discretized data. 109

110 110

111 111

112 112

113 12/27/2017 University of Waikato 113

114 12/27/2017 University of Waikato 114

115 12/27/2017 University of Waikato 115

116 12/27/2017 University of Waikato 116

117 12/27/2017 University of Waikato 117

118 12/27/2017 University of Waikato 118

119 12/27/2017 University of Waikato 119

120 12/27/2017 University of Waikato 120

121 12/27/2017 University of Waikato 121

122 12/27/2017 University of Waikato 122

123 Naïve Bayes Classifier Consider each attribute and class label as random variables Given a record with attributes (A 1, A 2,,A n ) Goal is to predict class C Specifically, we want to find the value of C that maximizes P(C A 1, A 2,,A n ) 123

124 Shape Dataset: 124

125 12/27/2017 University of Waikato 125

126 12/27/2017 University of Waikato 126

127 P(Triangle) = 5/14= 0.38 P(Square) = 9/14= 0.63 Color: Triangle Square Green 3 4 4/ /11 Original: P( A C) Laplace: P( A C) i N N COLORi ic OUTLINE classes DOT SHAPE c N N ic c 1 c c: number of GREEN DASHED NO? p: prior probability Yellow 0 1 1/ /11 Red 2 3 3/ /11 Outline: Triangle Square Dashed 4 5 5/ /11 Solid 1 2 2/ /11 4/7 *5/7 *3/7 *5/14 = Dot: Triangle Square Yes 3 4 4/ /11 No 2 3 3/ /11 3/11 *4/11 *7/11 *9/14 =

128 COLOR OUTLINE DOT SHAPE GREEN DASHED NO? Shapetest.csv 128

129 12/27/2017 University of Waikato 129

130 tp fn Confusion Matrix: fp tn True positive rate(tpr)/ Sensitivity,= False positive rate(fpr)/ Specificity,= No.of true positives No.of true positives+no.of false negatives No.of true negatives No.of true negatives+no.of false positives

131 tp fn Confusion Matrix: fp tn MCC (Matthews Correlation Coefficient): Measure of quality of binary classification

132 tn fp Confusion Matrix: fn tp True positive rate(tpr)/ Sensitivity,= False positive rate(fpr)/ Specificity,= No.of true positives No.of true positives+no.of false negatives No.of true negatives No.of true negatives+no.of false positives

133 tn fp Confusion Matrix: fn tp MCC (Matthews Correlation Coefficient): Measure of quality of binary classification

134 Kappa Statistic: Cohen s kappa statistic measures interrater reliability (sometimes called inter-observer agreement). Interrater reliability, or precision, happens when your data raters (or collectors) give the same score to the same data item. Step 1: Calculate P o (Observed Agreement). P 0 = (1+6)/14= 0.5 Step 2: Calculate P e (Expected Agreement). P(Triangle)=(5/14)*(4/14) P(Square)=(9/14)*(10/14) P e = (90/196)+(20/196)= K= ( )/( )=

135 STATUS FLOOR DEPT. OFFICE-SIZE RECYCLING- BIN? faculty four CS medium yes student four EE large yes staff five CS medium no student three EE small yes staff four CS medium no STATUS=student, FLOOR=four, DEPT. =CS, OFFICE SIZE=small Recycling Bin=? 135

136 Lets try with Iris dataset! 12/27/2017 University of Waikato 136

137 12/27/2017 University of Waikato 137

138 12/27/2017 University of Waikato 138

139 ROC Curve ROC: Receiver Operating Characteristic. Developed by British in World War II as part of Chain Home radar system. Used to analyze radar data to differentiate between enemy aircraft and signal noise. It is a performance graphing method. A plot of True Positive Rates and False Positive Rates. Used for evaluating data mining schemes. 139

140 ROC Curve 140

141 Example ROC Curve 141

142 Example ROC Curve 142

143 Why we need ROC curve? Consider a scenario: Design a ML tool. Training Data: Training Data Class: Should be test conducted for cancer by doctor? Create model. Tool will assign the patient a score between 0 and 1. High Score-? Tool is confident about the risk that patient has cancer. Low Score-?Tool is confident that patient is not at risk of having cancer. Test model. What evaluation measure-?. Before you measure anything, make a choicefamily history, age, weight, etc. Patient end up having cancer or not. True Positive Rate: How many ill people were recommended test? False Positive Rate: How many not-ill people were recommended test? False Negative Rate: How many ill people were not recommended test? True Negative Rate: How many not-ill people were not recommended test? Goal: To maximize TP, TN Rate and to minimize FP, FN Rate. Should not be test conducted for cancer by doctor? what threshold score do you use to decide whether or not patient needs test? 143

144 Consider a scenario: Design a ML tool. Should be tested conducted for cancer by doctor Training Data: family history, age, weight, etc. Training Data Class: Patient end up having cancer or not. Create model. Tool will assign the patient a score between 0 and 1. High Score-? Tool is confident about the risk of having cancer Low Score-? Tool Tool is is confident confident that that patient patient is is not not at at risk risk of of having having cancer. cancer. Test model. Should not be tested conducted for cancer by doctor What evaluation measure-?. Goal: To maximize TP, TN Rate and to minimize FP, FN Rate. Before you measure anything, make a choice- what threshold score do you use to decide whether or not patient needs test? As everyone with non-zero score has some risk. Low Threshold-?. Lot of Tests. High Threshold-?Ȯnly people with cancer will get tested. But there would be false negatives as well. (A lot of people with cancer would not be tested)

145 Non-diseased cases Diseased cases Threshold Test result value or subjective judgement of likelihood that case is diseased 145

146 Non-diseased cases Diseased cases more typically: Test result value or subjective judgement of likelihood that case is diseased 146

147 TPF, sensitivity Non-diseased cases Diseased cases Threshold less aggressive mindset FPF, 1-specificity 147

148 TPF, sensitivity Non-diseased cases Threshold moderate mindset Diseased cases FPF, 1-specificity 148

149 TPF, sensitivity Non-diseased cases more aggressive mindset Threshold Diseased cases FPF, 1-specificity 149

150 TPF, sensitivity Non-diseased cases Entire ROC curve Threshold Diseased cases FPF, 1-specificity 150

151 TPF, sensitivity Entire ROC curve Reader Skill and/or Level of Technology FPF, 1-specificity 151

152 Sensitivity: Refers to the test's ability to correctly detect ill patients who have cancer. Sensitivity = No.of true positives No.of true positives+no.of false negatives = probability of positive test given that patient is ill Specificity: Refers to the test's ability to correctly reject healthy patients who do not have cancer. Specificity = No.of true negatives No.of true negatives+no.of false positives = probability of negative test given that patent is not ill. 152

153 True positive rate (TPR) = False positive rate (FPR) = No.of true positives No.of true positives+no.of false negatives No.of false positives No.of true negatives+no.of false positives Move threshold from high to low. True positive rate increases (you test a higher proportion of those who do actually have cancer ) False positive rate increases (you incorrectly tell more people to get tested when they don t need to). 153

154 As you step through the threshold values from high to low, you put dots on the above graph from left to right - joining up the dots gives the ROC curve. 12/27/2017 University of Waikato 154

155 Score: 155

156

157 As you step through the threshold values from high to low, you put dots on the above graph from left to right - joining up the dots gives the ROC curve. 12/27/2017 University of Waikato 157

158 Comparing different classifiers: ROC curves provide a better look at where different learners minimize cost Which curve is better? Area under ROC curve: depicts how good classifier is? 158

159 Precision-Recall Curve 159

Introduction to Classification, aka Machine Learning

Introduction to Classification, aka Machine Learning Introduction to Classification, aka Machine Learning Classification: Definition Given a collection of examples (training set ) Each example is represented by a set of features, sometimes called attributes

More information

Introduction to Classification

Introduction to Classification Introduction to Classification Classification: Definition Given a collection of examples (training set ) Each example is represented by a set of features, sometimes called attributes Each example is to

More information

TOWARDS DATA-DRIVEN AUTONOMICS IN DATA CENTERS

TOWARDS DATA-DRIVEN AUTONOMICS IN DATA CENTERS TOWARDS DATA-DRIVEN AUTONOMICS IN DATA CENTERS ALINA SIRBU, OZALP BABAOGLU SUMMARIZED BY ARDA GUMUSALAN MOTIVATION 2 MOTIVATION Human-interaction-dependent data centers are not sustainable for future data

More information

CS Machine Learning

CS Machine Learning CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing

More information

Performance Analysis of Various Data Mining Techniques on Banknote Authentication

Performance Analysis of Various Data Mining Techniques on Banknote Authentication International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 5 Issue 2 February 2016 PP.62-71 Performance Analysis of Various Data Mining Techniques on

More information

Analytical Study of Some Selected Classification Algorithms in WEKA Using Real Crime Data

Analytical Study of Some Selected Classification Algorithms in WEKA Using Real Crime Data Analytical Study of Some Selected Classification Algorithms in WEKA Using Real Crime Data Obuandike Georgina N. Department of Mathematical Sciences and IT Federal University Dutsinma Katsina state, Nigeria

More information

A COMPARATIVE ANALYSIS OF META AND TREE CLASSIFICATION ALGORITHMS USING WEKA

A COMPARATIVE ANALYSIS OF META AND TREE CLASSIFICATION ALGORITHMS USING WEKA A COMPARATIVE ANALYSIS OF META AND TREE CLASSIFICATION ALGORITHMS USING WEKA T.Sathya Devi 1, Dr.K.Meenakshi Sundaram 2, (Sathya.kgm24@gmail.com 1, lecturekms@yahoo.com 2 ) 1 (M.Phil Scholar, Department

More information

Analysis of Different Classifiers for Medical Dataset using Various Measures

Analysis of Different Classifiers for Medical Dataset using Various Measures Analysis of Different for Medical Dataset using Various Measures Payal Dhakate ME Student, Pune, India. K. Rajeswari Associate Professor Pune,India Deepa Abin Assistant Professor, Pune, India ABSTRACT

More information

Arrhythmia Classification for Heart Attack Prediction Michelle Jin

Arrhythmia Classification for Heart Attack Prediction Michelle Jin Arrhythmia Classification for Heart Attack Prediction Michelle Jin Introduction Proper classification of heart abnormalities can lead to significant improvements in predictions of heart failures. The variety

More information

Session 1: Gesture Recognition & Machine Learning Fundamentals

Session 1: Gesture Recognition & Machine Learning Fundamentals IAP Gesture Recognition Workshop Session 1: Gesture Recognition & Machine Learning Fundamentals Nicholas Gillian Responsive Environments, MIT Media Lab Tuesday 8th January, 2013 My Research My Research

More information

Cost-Sensitive Learning and the Class Imbalance Problem

Cost-Sensitive Learning and the Class Imbalance Problem To appear in Encyclopedia of Machine Learning. C. Sammut (Ed.). Springer. 2008 Cost-Sensitive Learning and the Class Imbalance Problem Charles X. Ling, Victor S. Sheng The University of Western Ontario,

More information

Course 395: Machine Learning - Lectures

Course 395: Machine Learning - Lectures Course 395: Machine Learning - Lectures Lecture 1-2: Concept Learning (M. Pantic) Lecture 3-4: Decision Trees & CBC Intro (M. Pantic & S. Petridis) Lecture 5-6: Evaluating Hypotheses (S. Petridis) Lecture

More information

WEKA tutorial exercises

WEKA tutorial exercises WEKA tutorial exercises These tutorial exercises introduce WEKA and ask you to try out several machine learning, visualization, and preprocessing methods using a wide variety of datasets: Learners: decision

More information

Evaluation and Comparison of Performance of different Classifiers

Evaluation and Comparison of Performance of different Classifiers Evaluation and Comparison of Performance of different Classifiers Bhavana Kumari 1, Vishal Shrivastava 2 ACE&IT, Jaipur Abstract:- Many companies like insurance, credit card, bank, retail industry require

More information

I400 Health Informatics Data Mining Instructions (KP Project)

I400 Health Informatics Data Mining Instructions (KP Project) I400 Health Informatics Data Mining Instructions (KP Project) Casey Bennett Spring 2014 Indiana University 1) Import: First, we need to import the data into Knime. add CSV Reader Node (under IO>>Read)

More information

Optimization of Naïve Bayes Data Mining Classification Algorithm

Optimization of Naïve Bayes Data Mining Classification Algorithm Optimization of Naïve Bayes Data Mining Classification Algorithm Maneesh Singhal #1, Ramashankar Sharma #2 Department of Computer Engineering, University College of Engineering, Rajasthan Technical University,

More information

Childhood Obesity epidemic analysis using classification algorithms

Childhood Obesity epidemic analysis using classification algorithms Childhood Obesity epidemic analysis using classification algorithms Suguna. M M.Phil. Scholar Trichy, Tamilnadu, India suguna15.9@gmail.com Abstract Obesity is the one of the most serious public health

More information

Dudon Wai Georgia Institute of Technology CS 7641: Machine Learning Atlanta, GA

Dudon Wai Georgia Institute of Technology CS 7641: Machine Learning Atlanta, GA Adult Income and Letter Recognition - Supervised Learning Report An objective look at classifier performance for predicting adult income and Letter Recognition Dudon Wai Georgia Institute of Technology

More information

Random Under-Sampling Ensemble Methods for Highly Imbalanced Rare Disease Classification

Random Under-Sampling Ensemble Methods for Highly Imbalanced Rare Disease Classification 54 Int'l Conf. Data Mining DMIN'16 Random Under-Sampling Ensemble Methods for Highly Imbalanced Rare Disease Classification Dong Dai, and Shaowen Hua Abstract Classification on imbalanced data presents

More information

Classification of Arrhythmia Using Machine Learning Techniques

Classification of Arrhythmia Using Machine Learning Techniques Classification of Arrhythmia Using Machine Learning Techniques THARA SOMAN PATRICK O. BOBBIE School of Computing and Software Engineering Southern Polytechnic State University (SPSU) 1 S. Marietta Parkway,

More information

Data Mining: A Prediction for Academic Performance Improvement of Science Students using Classification

Data Mining: A Prediction for Academic Performance Improvement of Science Students using Classification Data Mining: A Prediction for Academic Performance Improvement of Science Students using Classification I.A Ganiyu Department of Computer Science, Ramon Adedoyin College of Science and Technology, Oduduwa

More information

Learning From the Past with Experiment Databases

Learning From the Past with Experiment Databases Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University

More information

Learning Bayes Networks

Learning Bayes Networks Learning Bayes Networks 6.034 Based on Russell & Norvig, Artificial Intelligence:A Modern Approach, 2nd ed., 2003 and D. Heckerman. A Tutorial on Learning with Bayesian Networks. In Learning in Graphical

More information

36-350: Data Mining. Fall Lectures: Monday, Wednesday and Friday, 10:30 11:20, Porter Hall 226B

36-350: Data Mining. Fall Lectures: Monday, Wednesday and Friday, 10:30 11:20, Porter Hall 226B 36-350: Data Mining Fall 2009 Instructor: Cosma Shalizi, Statistics Dept., Baker Hall 229C, cshalizi@stat.cmu.edu Teaching Assistant: Joseph Richards, jwrichar@stat.cmu.edu Lectures: Monday, Wednesday

More information

Session 7: Face Detection (cont.)

Session 7: Face Detection (cont.) Session 7: Face Detection (cont.) John Magee 8 February 2017 Slides courtesy of Diane H. Theriault Question of the Day: How can we find faces in images? Face Detection Compute features in the image Apply

More information

Linear Models Continued: Perceptron & Logistic Regression

Linear Models Continued: Perceptron & Logistic Regression Linear Models Continued: Perceptron & Logistic Regression CMSC 723 / LING 723 / INST 725 Marine Carpuat Slides credit: Graham Neubig, Jacob Eisenstein Linear Models for Classification Feature function

More information

Classifying Breast Cancer By Using Decision Tree Algorithms

Classifying Breast Cancer By Using Decision Tree Algorithms Classifying Breast Cancer By Using Decision Tree Algorithms Nusaibah AL-SALIHY, Turgay IBRIKCI (Presenter) Cukurova University, TURKEY What Is A Decision Tree? Why A Decision Tree? Why Decision TreeClassification?

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

A Practical Tour of Ensemble (Machine) Learning

A Practical Tour of Ensemble (Machine) Learning A Practical Tour of Ensemble (Machine) Learning Nima Hejazi Evan Muzzall Division of Biostatistics, University of California, Berkeley D-Lab, University of California, Berkeley slides: https://googl/wwaqc

More information

1 Subject. 2 Dataset. 3 Descriptive statistics. 3.1 Data importation. SIPINA proposes some descriptive statistics functionalities.

1 Subject. 2 Dataset. 3 Descriptive statistics. 3.1 Data importation. SIPINA proposes some descriptive statistics functionalities. 1 Subject proposes some descriptive statistics functionalities. In itself, the information is not really exceptional; there is a large number of freeware which do that. It becomes more interesting when

More information

A Few Useful Things to Know about Machine Learning. Pedro Domingos Department of Computer Science and Engineering University of Washington" 2012"

A Few Useful Things to Know about Machine Learning. Pedro Domingos Department of Computer Science and Engineering University of Washington 2012 A Few Useful Things to Know about Machine Learning Pedro Domingos Department of Computer Science and Engineering University of Washington 2012 A Few Useful Things to Know about Machine Learning Machine

More information

The Study and Analysis of Classification Algorithm for Animal Kingdom Dataset

The Study and Analysis of Classification Algorithm for Animal Kingdom Dataset www.seipub.org/ie Information Engineering Volume 2 Issue 1, March 2013 The Study and Analysis of Classification Algorithm for Animal Kingdom Dataset E. Bhuvaneswari *1, V. R. Sarma Dhulipala 2 Assistant

More information

Rule Learning With Negation: Issues Regarding Effectiveness

Rule Learning With Negation: Issues Regarding Effectiveness Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United

More information

CS545 Machine Learning

CS545 Machine Learning Machine learning and related fields CS545 Machine Learning Course Introduction Machine learning: the construction and study of systems that learn from data. Pattern recognition: the same field, different

More information

Machine Learning for NLP

Machine Learning for NLP Natural Language Processing SoSe 2014 Machine Learning for NLP Dr. Mariana Neves April 30th, 2014 (based on the slides of Dr. Saeedeh Momtazi) Introduction Field of study that gives computers the ability

More information

Assignment 6 (Sol.) Introduction to Machine Learning Prof. B. Ravindran

Assignment 6 (Sol.) Introduction to Machine Learning Prof. B. Ravindran Assignment 6 (Sol.) Introduction to Machine Learning Prof. B. Ravindran 1. Assume that you are given a data set and a neural network model trained on the data set. You are asked to build a decision tree

More information

Learning Imbalanced Data with Random Forests

Learning Imbalanced Data with Random Forests Learning Imbalanced Data with Random Forests Chao Chen (Stat., UC Berkeley) chenchao@stat.berkeley.edu Andy Liaw (Merck Research Labs) andy_liaw@merck.com Leo Breiman (Stat., UC Berkeley) leo@stat.berkeley.edu

More information

Classification with Deep Belief Networks. HussamHebbo Jae Won Kim

Classification with Deep Belief Networks. HussamHebbo Jae Won Kim Classification with Deep Belief Networks HussamHebbo Jae Won Kim Table of Contents Introduction... 3 Neural Networks... 3 Perceptron... 3 Backpropagation... 4 Deep Belief Networks (RBM, Sigmoid Belief

More information

P(A, B) = P(A B) = P(A) + P(B) - P(A B)

P(A, B) = P(A B) = P(A) + P(B) - P(A B) AND Probability P(A, B) = P(A B) = P(A) + P(B) - P(A B) P(A B) = P(A) + P(B) - P(A B) Area = Probability of Event AND Probability P(A, B) = P(A B) = P(A) + P(B) - P(A B) If, and only if, A and B are independent,

More information

White Paper. Using Sentiment Analysis for Gaining Actionable Insights

White Paper. Using Sentiment Analysis for Gaining Actionable Insights corevalue.net info@corevalue.net White Paper Using Sentiment Analysis for Gaining Actionable Insights Sentiment analysis is a growing business trend that allows companies to better understand their brand,

More information

Scaling Quality On Quora Using Machine Learning

Scaling Quality On Quora Using Machine Learning Scaling Quality On Quora Using Machine Learning Nikhil Garg @nikhilgarg28 @Quora @QconSF 11/7/16 Goals Of The Talk Introducing specific product problems we need to solve to stay high-quality Describing

More information

Decision Boundary. Hemant Ishwaran and J. Sunil Rao

Decision Boundary. Hemant Ishwaran and J. Sunil Rao 32 Decision Trees, Advanced Techniques in Constructing define impurity using the log-rank test. As in CART, growing a tree by reducing impurity ensures that terminal nodes are populated by individuals

More information

CSC-272 Exam #2 March 20, 2015

CSC-272 Exam #2 March 20, 2015 CSC-272 Exam #2 March 20, 2015 Name Questions are weighted as indicated. Show your work and state your assumptions for partial credit consideration. Unless explicitly stated, there are NO intended errors

More information

A study of the NIPS feature selection challenge

A study of the NIPS feature selection challenge A study of the NIPS feature selection challenge Nicholas Johnson November 29, 2009 Abstract The 2003 Nips Feature extraction challenge was dominated by Bayesian approaches developed by the team of Radford

More information

A COMPARATIVE STUDY FOR PREDICTING STUDENT S ACADEMIC PERFORMANCE USING BAYESIAN NETWORK CLASSIFIERS

A COMPARATIVE STUDY FOR PREDICTING STUDENT S ACADEMIC PERFORMANCE USING BAYESIAN NETWORK CLASSIFIERS IOSR Journal of Engineering (IOSRJEN) e-issn: 2250-3021, p-issn: 2278-8719 Vol. 3, Issue 2 (Feb. 2013), V1 PP 37-42 A COMPARATIVE STUDY FOR PREDICTING STUDENT S ACADEMIC PERFORMANCE USING BAYESIAN NETWORK

More information

Rule Learning with Negation: Issues Regarding Effectiveness

Rule Learning with Negation: Issues Regarding Effectiveness Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX

More information

Forecasting Statewide Test Performance and Adequate Yearly Progress from District Assessments

Forecasting Statewide Test Performance and Adequate Yearly Progress from District Assessments Research Paper Forecasting Statewide Test Performance and Adequate Yearly Progress from District Assessments by John Richard Bergan, Ph.D. and John Robert Bergan, Ph.D. Assessment Technology, Incorporated

More information

1. Subject. 2. Dataset. Resampling approaches for prediction error estimation.

1. Subject. 2. Dataset. Resampling approaches for prediction error estimation. 1. Subject Resampling approaches for prediction error estimation. The ability to predict correctly is one of the most important criteria to evaluate classifiers in supervised learning. The preferred indicator

More information

Predictive Analysis of Text: Concepts, Features, and Instances

Predictive Analysis of Text: Concepts, Features, and Instances of Text: Concepts, Features, and Instances Jaime Arguello jarguell@email.unc.edu August 26, 2015 of Text Objective: developing and evaluating computer programs that automatically detect a particular concept

More information

Machine Learning in Patent Analytics:: Binary Classification for Prioritizing Search Results

Machine Learning in Patent Analytics:: Binary Classification for Prioritizing Search Results Machine Learning in Patent Analytics:: Binary Classification for Prioritizing Search Results Anthony Trippe Managing Director, Patinformatics, LLC Patent Information Fair & Conference November 10, 2017

More information

SOFTWARE ARCHITECTURE FOR BUILDING INTELLIGENT USER INTERFACES BASED ON DATA MINING INTEGRATION

SOFTWARE ARCHITECTURE FOR BUILDING INTELLIGENT USER INTERFACES BASED ON DATA MINING INTEGRATION International Journal of Computer Science and Applications, Technomathematics Research Foundation Vol. 8, No. 1, pp. 71 82, 2011 SOFTWARE ARCHITECTURE FOR BUILDING INTELLIGENT USER INTERFACES BASED ON

More information

Cross-Domain Video Concept Detection Using Adaptive SVMs

Cross-Domain Video Concept Detection Using Adaptive SVMs Cross-Domain Video Concept Detection Using Adaptive SVMs AUTHORS: JUN YANG, RONG YAN, ALEXANDER G. HAUPTMANN PRESENTATION: JESSE DAVIS CS 3710 VISUAL RECOGNITION Problem-Idea-Challenges Address accuracy

More information

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and

More information

Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers

Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers Improving Accelerometer-Based Activity Recognition by Using Ensemble of Classifiers Tahani Daghistani, Riyad Alshammari College of Public Health and Health Informatics King Saud Bin Abdulaziz University

More information

Lecture 1: Introduc4on

Lecture 1: Introduc4on CSC2515 Spring 2014 Introduc4on to Machine Learning Lecture 1: Introduc4on All lecture slides will be available as.pdf on the course website: http://www.cs.toronto.edu/~urtasun/courses/csc2515/csc2515_winter15.html

More information

!"#$%#&'()$*#+','()#$(-+,./01)

!#$%#&'()$*#+','()#$(-+,./01) Questions!"#$%#&'()$*#+','()#$(-+,./01) Since induction is fallible, it is necessary to be able to assess its reliability!! Typical questions:! AgroParisTech! What is the true performance of my (learned)

More information

Text Categorization and Support Vector Machines

Text Categorization and Support Vector Machines Text Categorization and Support Vector Machines István Pilászy Department of Measurement and Information Systems Budapest University of Technology and Economics e-mail: pila@mit.bme.hu Abstract: Text categorization

More information

USING DATA MINING METHODS KNOWLEDGE DISCOVERY FOR TEXT MINING

USING DATA MINING METHODS KNOWLEDGE DISCOVERY FOR TEXT MINING USING DATA MINING METHODS KNOWLEDGE DISCOVERY FOR TEXT MINING D.M.Kulkarni 1, S.K.Shirgave 2 1, 2 IT Department Dkte s TEI Ichalkaranji (Maharashtra), India Abstract Many data mining techniques have been

More information

K-Means Clustering. By Susan L. Miertschin

K-Means Clustering. By Susan L. Miertschin K-Means Clustering By Susan L. Miertschin 1 Data Mining - Task Types Classification Clustering Discovering Association Rules Discovering Sequential Patterns Sequence Analysis Regression Detecting Deviations

More information

Utility Theory, Minimum Effort, and Predictive Coding

Utility Theory, Minimum Effort, and Predictive Coding Utility Theory, Minimum Effort, and Predictive Coding Fabrizio Sebastiani (Joint work with Giacomo Berardi and Andrea Esuli) Istituto di Scienza e Tecnologie dell Informazione Consiglio Nazionale delle

More information

Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches

Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches Qandeel Tariq, Alex Kolchinski, Richard Davis December 6, 206 Introduction This paper

More information

Paper Examining Higher Education Performance Metrics with SAS Enterprise Miner and SAS Visual Analytics

Paper Examining Higher Education Performance Metrics with SAS Enterprise Miner and SAS Visual Analytics ABSTRACT Paper 788-2017 Examining Higher Education Performance Metrics with SAS Enterprise Miner and SAS Visual Analytics Taylor Blaetz, M.S., Western Kentucky University; Bowling Green, KY Tuesdi Helbig,

More information

(-: (-: SMILES :-) :-)

(-: (-: SMILES :-) :-) (-: (-: SMILES :-) :-) A Multi-purpose Learning System Vicent Estruch, Cèsar Ferri, José Hernández-Orallo, M.José Ramírez-Quintana {vestruch, cferri, jorallo, mramirez}@dsic.upv.es Dep. de Sistemes Informàtics

More information

Prediction Of Student Performance Using Weka Tool

Prediction Of Student Performance Using Weka Tool Prediction Of Student Performance Using Weka Tool Gurmeet Kaur 1, Williamjit Singh 2 1 Student of M.tech (CE), Punjabi university, Patiala 2 (Asst. Professor) Department of CE, Punjabi University, Patiala

More information

18 LEARNING FROM EXAMPLES

18 LEARNING FROM EXAMPLES 18 LEARNING FROM EXAMPLES An intelligent agent may have to learn, for instance, the following components: A direct mapping from conditions on the current state to actions A means to infer relevant properties

More information

Don t Get Kicked - Machine Learning Predictions for Car Buying

Don t Get Kicked - Machine Learning Predictions for Car Buying STANFORD UNIVERSITY, CS229 - MACHINE LEARNING Don t Get Kicked - Machine Learning Predictions for Car Buying Albert Ho, Robert Romano, Xin Alice Wu December 14, 2012 1 Introduction When you go to an auto

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Computer Vision for Card Games

Computer Vision for Card Games Computer Vision for Card Games Matias Castillo matiasct@stanford.edu Benjamin Goeing bgoeing@stanford.edu Jesper Westell jesperw@stanford.edu Abstract For this project, we designed a computer vision program

More information

IMBALANCED data sets (IDS) correspond to domains

IMBALANCED data sets (IDS) correspond to domains Diversity Analysis on Imbalanced Data Sets by Using Ensemble Models Shuo Wang and Xin Yao Abstract Many real-world applications have problems when learning from imbalanced data sets, such as medical diagnosis,

More information

Ensemble Classifier for Solving Credit Scoring Problems

Ensemble Classifier for Solving Credit Scoring Problems Ensemble Classifier for Solving Credit Scoring Problems Maciej Zięba and Jerzy Świątek Wroclaw University of Technology, Faculty of Computer Science and Management, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław,

More information

6.034 Notes: Section 13.1

6.034 Notes: Section 13.1 6.034 Notes: Section 13.1 Slide 13.1.1 Now that we have looked at the basic mathematical techniques for minimizing the training error of a neural net, we should step back and look at the whole approach

More information

Identifying Localization in Reviews of Argument Diagrams

Identifying Localization in Reviews of Argument Diagrams Identifying Localization in Reviews of Argument Diagrams Huy Nguyen 1 Diane Litman 1,2 1 Computer Science Department 2 Learning Research and Development Center at University of Pittsburgh ArgumentPeer

More information

Classification with class imbalance problem: A Review

Classification with class imbalance problem: A Review Int. J. Advance Soft Compu. Appl, Vol. 7, No. 3, November 2015 ISSN 2074-8523 Classification with class imbalance problem: A Review Aida Ali 1,2, Siti Mariyam Shamsuddin 1,2, and Anca L. Ralescu 3 1 UTM

More information

Assignment #6: Neural Networks (with Tensorflow) CSCI 374 Fall 2017 Oberlin College Due: Tuesday November 21 at 11:59 PM

Assignment #6: Neural Networks (with Tensorflow) CSCI 374 Fall 2017 Oberlin College Due: Tuesday November 21 at 11:59 PM Background Assignment #6: Neural Networks (with Tensorflow) CSCI 374 Fall 2017 Oberlin College Due: Tuesday November 21 at 11:59 PM Our final assignment this semester has three main goals: 1. Implement

More information

Supervised learning can be done by choosing the hypothesis that is most probable given the data: = arg max ) = arg max

Supervised learning can be done by choosing the hypothesis that is most probable given the data: = arg max ) = arg max The learning problem is called realizable if the hypothesis space contains the true function; otherwise it is unrealizable On the other hand, in the name of better generalization ability it may be sensible

More information

Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions

Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions , October 20-22, 2010, San Francisco, USA Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions N.Gayatri, S.Nickolas, A.V.Reddy Abstract The importance

More information

Introduction to Pattern Recognition

Introduction to Pattern Recognition Introduction to Pattern Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Fall 2017 CS 551, Fall 2017 c 2017, Selim Aksoy (Bilkent University)

More information

Computer Security: A Machine Learning Approach

Computer Security: A Machine Learning Approach Computer Security: A Machine Learning Approach We analyze two learning algorithms, NBTree and VFI, for the task of detecting intrusions. SANDEEP V. SABNANI AND ANDREAS FUCHSBERGER Produced by the Information

More information

Assessing Medical Effectiveness in Cardio- Thoracic Surgery

Assessing Medical Effectiveness in Cardio- Thoracic Surgery 4 th Workshop on Efficiency and Productivity Analysis Efficiency in the Health Sector Ricardo A. S. Castro Faculdade de Engenharia, Universidade do Porto Pedro N. Oliveira Instituto de Ciências Biomédicas

More information

Bird Species Identification from an Image

Bird Species Identification from an Image Bird Species Identification from an Image Aditya Bhandari, 1 Ameya Joshi, 2 Rohit Patki 3 1 Department of Computer Science, Stanford University 2 Department of Electrical Engineering, Stanford University

More information

Machine Learning and Applications in Finance

Machine Learning and Applications in Finance Machine Learning and Applications in Finance Christian Hesse 1,2,* 1 Autobahn Equity Europe, Global Markets Equity, Deutsche Bank AG, London, UK christian-a.hesse@db.com 2 Department of Computer Science,

More information

Linear Regression. Chapter Introduction

Linear Regression. Chapter Introduction Chapter 9 Linear Regression 9.1 Introduction In this class, we have looked at a variety of di erent models and learning methods, such as finite state machines, sequence models, and classification methods.

More information

CLASSIFICATION AND COMPARATIVE STUDY OF DATA MINING CLASSIFIERS WITH FEATURE SELECTION ON BINOMIAL DATA SET

CLASSIFICATION AND COMPARATIVE STUDY OF DATA MINING CLASSIFIERS WITH FEATURE SELECTION ON BINOMIAL DATA SET Volume 3, No. 5, May 212 Journal of Global Research in Computer Science RESEARCH PAPER Available Online at www.jgrcs.info CLASSIFICATION AND COMPARATIVE STUDY OF DATA MINING CLASSIFIERS WITH FEATURE SELECTION

More information

Investigation of Property Valuation Models Based on Decision Tree Ensembles Built over Noised Data

Investigation of Property Valuation Models Based on Decision Tree Ensembles Built over Noised Data Investigation of Property Valuation Models Based on Decision Tree Ensembles Built over Noised Data Tadeusz Lasota 1, Tomasz Łuczak 2, Michał Niemczyk 2, Michał Olszewski 2, Bogdan Trawiński 2 1 Wrocław

More information

Guido Boella Dipartimento di Informatica Università di Torino FP7-ICT-2013-SME-DCA

Guido Boella Dipartimento di Informatica Università di Torino FP7-ICT-2013-SME-DCA EuroVoc classifier Guido Boella Dipartimento di Informatica Università di Torino FP7-ICT-2013-SME-DCA Overview Introduction Background Our approach Pre-processing of the texts Evaluation Introduction Classification

More information

ST 562: Data Mining with SAS Enterprise Miner

ST 562: Data Mining with SAS Enterprise Miner ST 562: Data Mining with SAS Enterprise Miner In Workflow 1. 17ST GR Director of Curriculum (demarti4@ncsu.edu; bondell@stat.ncsu.edu) 2. 17ST Grad Head (demarti4@ncsu.edu; bondell@stat.ncsu.edu; fuentes@ncsu.edu)

More information

Syllabus Data Mining for Business Analytics - Managerial INFO-GB.3336, Spring 2018

Syllabus Data Mining for Business Analytics - Managerial INFO-GB.3336, Spring 2018 Syllabus Data Mining for Business Analytics - Managerial INFO-GB.3336, Spring 2018 Course information When: Mondays and Wednesdays 3-4:20pm Where: KMEC 3-65 Professor Manuel Arriaga Email: marriaga@stern.nyu.edu

More information

Lecture 1. Introduction Bastian Leibe Visual Computing Institute RWTH Aachen University

Lecture 1. Introduction Bastian Leibe Visual Computing Institute RWTH Aachen University Advanced Machine Learning Lecture 1 Introduction 20.10.2015 Bastian Leibe Visual Computing Institute RWTH Aachen University http://www.vision.rwth-aachen.de/ leibe@vision.rwth-aachen.de Organization Lecturer

More information

A Hybrid Model of Soft Computing Technique for Software Fault Prediction

A Hybrid Model of Soft Computing Technique for Software Fault Prediction Research Article International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347-5161 2014 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Anurag

More information

Multiple classifiers. JERZY STEFANOWSKI Institute of Computing Sciences Poznań University of Technology. Doctoral School, Catania-Troina, April, 2008

Multiple classifiers. JERZY STEFANOWSKI Institute of Computing Sciences Poznań University of Technology. Doctoral School, Catania-Troina, April, 2008 Multiple classifiers JERZY STEFANOWSKI Institute of Computing Sciences Poznań University of Technology Doctoral School, Catania-Troina, April, 2008 Outline of the presentation 1. Introduction 2. Why do

More information

Negative News No More: Classifying News Article Headlines

Negative News No More: Classifying News Article Headlines Negative News No More: Classifying News Article Headlines Karianne Bergen and Leilani Gilpin kbergen@stanford.edu lgilpin@stanford.edu December 14, 2012 1 Introduction The goal of this project is to develop

More information

Analyzing neural time series data: Theory and practice

Analyzing neural time series data: Theory and practice Page i Analyzing neural time series data: Theory and practice Mike X Cohen MIT Press, early 2014 Page ii Contents Section 1: Introductions Chapter 1: The purpose of this book, who should read it, and how

More information

Multiple classifiers

Multiple classifiers Multiple classifiers JERZY STEFANOWSKI Institute of Computing Sciences Poznań University of Technology Zajęcia dla TPD - ZED 2009 Oparte na wykładzie dla Doctoral School, Catania-Troina, April, 2008 Outline

More information

Unsupervised Learning and Dimensionality Reduction A Continued Study on Letter Recognition and Adult Income

Unsupervised Learning and Dimensionality Reduction A Continued Study on Letter Recognition and Adult Income Unsupervised Learning and Dimensionality Reduction A Continued Study on Letter Recognition and Adult Income Dudon Wai, dwai3 Georgia Institute of Technology CS 7641: Machine Learning Abstract: This paper

More information

Analysis of Clustering and Classification Methods for Actionable Knowledge

Analysis of Clustering and Classification Methods for Actionable Knowledge Available online at www.sciencedirect.com ScienceDirect Materials Today: Proceedings XX (2016) XXX XXX www.materialstoday.com/proceedings PMME 2016 Analysis of Clustering and Classification Methods for

More information

Machine Learning Tom M. Mitchell Machine Learning Department Carnegie Mellon University. January 11, 2011

Machine Learning Tom M. Mitchell Machine Learning Department Carnegie Mellon University. January 11, 2011 Machine Learning 10-701 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 11, 2011 Today: What is machine learning? Decision tree learning Course logistics Readings: The Discipline

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

CS Data Science and Visualization Spring 2016

CS Data Science and Visualization Spring 2016 CS 207 - Data Science and Visualization Spring 2016 Professor: Sorelle Friedler sorelle@cs.haverford.edu An introduction to techniques for the automated and human-assisted analysis of data sets. These

More information

Artificial Neural Networks

Artificial Neural Networks Artificial Neural Networks Outline Introduction to Neural Network Introduction to Artificial Neural Network Properties of Artificial Neural Network Applications of Artificial Neural Network Demo Neural

More information

Combating the Class Imbalance Problem in Small Sample Data Sets

Combating the Class Imbalance Problem in Small Sample Data Sets Combating the Class Imbalance Problem in Small Sample Data Sets Michael Wasikowski Submitted to the Department of Electrical Engineering & Computer Science and the Graduate Faculty of the University of

More information