Data Mining and Knowledge Discovery: Practice Notes
|
|
|
- Avice Francine Wright
- 5 years ago
- Views:
Transcription
1 Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak
2 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization Algorithms Decision tree induction, entropy, information gain, overfitting, Occam s razor, model pruning, naïve Bayes classifier, KNN, association rules, support, confidence, numeric prediction, regression tree, model tree, heuristics vs. exhaustive search, predictive vs. descriptive DM Evaluation Train set, test set, accuracy, confusion matrix, cross validation, true positives, false positives, ROC space, AUC, error, precision, recall 2
3 DT induction graphically 3
4 DT induction graphically 4
5 DT induction graphically 5
6 DT induction graphically Language bias: DTs can only make perpendicular splits No conditions like A>B B A 6
7 Models with other language biases can also overfit (e.g. SVM) 7
8 Models with other language biases can also overfit (e.g. SVM) and need to be generalized 8
9 Model complexity and performance on train set 9
10 Performance on train and test set We reduce model complexity (increase generalization) to avoid overfitting to get more interpretable models (sacrifice some accuracy) 10
11 Occam s raisor Suppose there exist two explanations for a phenomena. In this case, the simpler one is usually better. Note: classifiers can/should also assign each prediction a confidence score. 11
12 Prediction confidence 6/7 examples in this leaf belong to the class Lenses=YES 1/7 belongs to the class Lenses=NO P(YES) = 6/7 = P Laplace (YES) = = /10 examples in this leaf belong to class Lenses=NO P(YES) = 0/10 = P Laplace (YES) = = * Laplace probability estimate is explained at Naïve Bayes. 12
13 How confident Person Age Prescription Astigmatic Tear_Rate Lenses P3 young hypermetrope no normal YES P9 pre-presbyopic myope no normal YES P12 pre-presbyopic hypermetrope no reduced NO P13 pre-presbyopic myope yes normal YES P15 pre-presbyopic hypermetrope yes normal NO P16 pre-presbyopic hypermetrope yes reduced NO P23 presbyopic hypermetrope yes normal NO Assign to each example in the test set a confidence score of assigning it to the class Lenses=YES. * Use Laplace estimate 13
14 How confident Sort descending 14
15 ROC curve and AUC Receiver Operating Characteristic curve (or ROC curve) is a plot of the true positive rate (TPr=Sensitivity=Recall) against the false positive rate (FPr) for different possible cutpoints. It shows the tradeoff between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity). The closer the curve to the top left corner, the more accurate the classifier. The diagonal represents a baseline classifier. 15
16 AUC - Area Under (ROC) Curve Performance is measured by the area under the ROC curve (AUC). An area of 1 represents a perfect classifier; an area of 0.5 represents a worthless classifier. The area under the curve (AUC) is equal to the probability that a classifier will rank a randomly chosen positive example higher than a randomly chosen negative example. 16
17 How confident Possible classifiers: 100 % confident TP=0, FP=0 TPr= 0, FPr=0 78 % confident TP=3, FP=2 TPr= 3/3 = 1, FPr= 2/4 = % confident TP=3, FP=4 TPr= 3/3 = 1, FPr= 4/4 = 1 0 % confident TP=3, FP=4 TPr= 3/3 = 1, FPr= 4/4 = 1 TPr = correctly classified positives / all positives FPr = negatives classified as positives / all negatives 17
18 Classifier to ROC Possible classifiers: 100 % confident TP=0, FP=0 TPr= 0, FPr=0 78 % confident TP=3, FP=2 TPr= 3/3 = 1, FPr= 2/4 = % confident TP=3, FP=4 TPr= 3/3 = 1, FPr= 4/4 = 1 0 % confident TP=3, FP=4 TPr= 3/3 = 1, FPr= 4/4 = 1 AUC of our classifier is An AUC close to 0.5 is a bad AUC. 18
19 ROC curve and AUC Actual class Confidence classifier forclass Y FP TP FPr TPr P1 Y 1 P2 Y 1 P3 Y 0.95 P4 Y 0.9 P5 Y 0.9 P6 N 0.85 P7 Y 0.8 P8 Y 0.6 P9 Y 0.55 P10 Y 0.55 P11 N 0.3 P12 N 0.25 P13 Y 0.25 P14 N 0.2 P15 N 0.1 P16 N 0.1 P17 N 0.1 P18 N 0 P19 N 0 P20 N 0 19
20 ROC curve and AUC Actual class Confidence classifier forclass Y FP TP FPr TPr P1 Y P2 Y P3 Y P4 Y P5 Y P6 N P7 Y P8 Y P9 Y P10 Y P11 N P12 N P13 Y P14 N P15 N P16 N P17 N P18 N P19 N P20 N
21 ROC curve and AUC Actual class Confidence classifier forclass Y FPr TPr P1 Y P2 Y P3 Y P4 Y P5 Y P6 N P7 Y P8 Y P9 Y P10 Y P11 N P12 N P13 Y P14 N P15 N P16 N P17 N P18 N P19 N P20 N Area Under (the convex) Curve AUC =
22 Predicting with Naïve Bayes Given Attribute-value data with nominal target variable Induce Build a Naïve Bayes classifier and estimate its performance on new data 22
23 Naïve Bayes classifier Assumption: conditional independence of attributes given the class. 23
24 Naïve Bayes classifier Assumption: conditional independence of attributes given the class. Will the spider catch these two ants? Color = white, Time = night Color = black, Size = large, Time = day 24
25 Naïve Bayes classifier -example 25
26 Estimating probability Relative frequency P(e) = e /n A disadvantage of using relative frequencies for probability estimation arises with small sample sizes, especially if they are either very close to zero, or very close to one. In our spider example: P(Time=day Class=NO) = = 0/3 = 0 e number times an event e happened n number of trials k number of possible outcomes 26
27 Relative frequency vs. Laplace estimate Relative frequency P(c) = n(c) /N A disadvantage of using relative frequencies for probability estimation arises with small sample sizes, especially if they are either very close to zero, or very close to one. In our spider example: P(Time=day caught=no) = = 0/3 = 0 n(c) number of examples where c is true N number of all examples k number of possible events Laplace estimate Assumes uniform prior distribution over the probabilities for each possible event P(c) = (n(c) + 1) / (N + k) In our spider example: P(Time=day caught=no) = (0+1)/(3+2) = 1/5 With lots of evidence approximates relative frequency If there were 300 cases when the spider didn t catch ants at night: P(Time=day caught=no) = (0+1)/(300+2) = 1/302 = With Laplace estimate probabilities can never be 0. 27
28 K-fold cross validation 1. The sample set is partitioned into K subsets ("folds") of about equal size 2. A single subset is retained as the validation data for testing the model (this subset is called the "testset"), and the remaining K - 1 subsets together are used as training data ("trainset"). 3. A model is trained on the trainset and its performance (accuracy or other performance measure) is evaluated on the testset 4. Model training and evaluation is repeated K times, with each of the K subsets used exactly once as the testset. 5. The average of all the accuracy estimations obtained after each iteration is the resulting accuracy estimation. 28
29 29
30 Discussion 1. Compare naïve Bayes and decision trees (similarities and differences). 2. Compare cross validation and testing on a separate test set. 3. Why do we prune decision trees? 4. What is discretization. 5. Why can t we always achieve 100% accuracy on the training set? 6. Compare Laplace estimate with relative frequency. 7. Why does Naïve Bayes work well (even if independence assumption is clearly violated)? 8. What are the benefits of using Laplace estimate instead of relative frequency for probability estimation in Naïve Bayes? 30
Data Mining and Knowledge Discovery Practice notes 2
Keywords Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si Data Attribute, example, attribute-value data, target variable, class, discretization Algorithms
Data Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2016/11/16 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
Data Mining and Knowledge Discovery Practice notes: Numeric Prediction, Association Rules
Keywords Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si Data Attribute, example, attribute-value data, target variable, class, discretization Algorithms
Data Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2016/01/12 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
Data Mining and Knowledge Discovery Practice notes: Numeric Prediction, Association Rules
Keywords Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 06/0/ Data Attribute, example, attribute-value data, target variable, class, discretization Algorithms
Data Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2016/01/12 1 Keywords Data Attribute, example, attribute-value data, target variable, class, discretization
Data Mining and Knowledge Discovery: Practice Notes
Data Mining and Knowledge Discovery: Practice Notes Petra Kralj Novak Petra.Kralj.Novak@ijs.si 2013/12/09 1 Practice plan 2013/11/11: Predictive data mining 1 Decision trees Evaluating classifiers 1: separate
Metrics for Performance Evaluation How to evaluate the performance of a model? Methods for Performance Evaluation How to obtain reliable estimates?
Model Evaluation Metrics for Performance Evaluation How to evaluate the performance of a model? Methods for Performance Evaluation How to obtain reliable estimates? Methods for Model Comparison How to
Classification. Instructor: Wei Ding
Classification Part II Instructor: Wei Ding Tan,Steinbach, Kumar Introduction to Data Mining 4/18/004 1 Practical Issues of Classification Underfitting and Overfitting Missing Values Costs of Classification
Evaluating Classifiers
Evaluating Classifiers Reading for this topic: T. Fawcett, An introduction to ROC analysis, Sections 1-4, 7 (linked from class website) Evaluating Classifiers What we want: Classifier that best predicts
Evaluating Classifiers
Evaluating Classifiers Reading for this topic: T. Fawcett, An introduction to ROC analysis, Sections 1-4, 7 (linked from class website) Evaluating Classifiers What we want: Classifier that best predicts
CS145: INTRODUCTION TO DATA MINING
CS145: INTRODUCTION TO DATA MINING 08: Classification Evaluation and Practical Issues Instructor: Yizhou Sun yzsun@cs.ucla.edu October 24, 2017 Learnt Prediction and Classification Methods Vector Data
List of Exercises: Data Mining 1 December 12th, 2015
List of Exercises: Data Mining 1 December 12th, 2015 1. We trained a model on a two-class balanced dataset using five-fold cross validation. One person calculated the performance of the classifier by measuring
Classification Part 4
Classification Part 4 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville Model Evaluation Metrics for Performance Evaluation How to evaluate
CS4491/CS 7265 BIG DATA ANALYTICS
CS4491/CS 7265 BIG DATA ANALYTICS EVALUATION * Some contents are adapted from Dr. Hung Huang and Dr. Chengkai Li at UT Arlington Dr. Mingon Kang Computer Science, Kennesaw State University Evaluation for
Probabilistic Classifiers DWML, /27
Probabilistic Classifiers DWML, 2007 1/27 Probabilistic Classifiers Conditional class probabilities Id. Savings Assets Income Credit risk 1 Medium High 75 Good 2 Low Low 50 Bad 3 High Medium 25 Bad 4 Medium
Data Mining Algorithms: Basic Methods
Algorithms: The basic methods Inferring rudimentary rules Data Mining Algorithms: Basic Methods Chapter 4 of Data Mining Statistical modeling Constructing decision trees Constructing rules Association
CS6375: Machine Learning Gautam Kunapuli. Mid-Term Review
Gautam Kunapuli Machine Learning Data is identically and independently distributed Goal is to learn a function that maps to Data is generated using an unknown function Learn a hypothesis that minimizes
Data Mining Classification: Alternative Techniques. Imbalanced Class Problem
Data Mining Classification: Alternative Techniques Imbalanced Class Problem Introduction to Data Mining, 2 nd Edition by Tan, Steinbach, Karpatne, Kumar Class Imbalance Problem Lots of classification problems
Evaluation Measures. Sebastian Pölsterl. April 28, Computer Aided Medical Procedures Technische Universität München
Evaluation Measures Sebastian Pölsterl Computer Aided Medical Procedures Technische Universität München April 28, 2015 Outline 1 Classification 1. Confusion Matrix 2. Receiver operating characteristics
DATA MINING LECTURE 11. Classification Basic Concepts Decision Trees Evaluation Nearest-Neighbor Classifier
DATA MINING LECTURE 11 Classification Basic Concepts Decision Trees Evaluation Nearest-Neighbor Classifier What is a hipster? Examples of hipster look A hipster is defined by facial hair Hipster or Hippie?
Weka ( )
Weka ( http://www.cs.waikato.ac.nz/ml/weka/ ) The phases in which classifier s design can be divided are reflected in WEKA s Explorer structure: Data pre-processing (filtering) and representation Supervised
DATA MINING LECTURE 9. Classification Basic Concepts Decision Trees Evaluation
DATA MINING LECTURE 9 Classification Basic Concepts Decision Trees Evaluation What is a hipster? Examples of hipster look A hipster is defined by facial hair Hipster or Hippie? Facial hair alone is not
MEASURING CLASSIFIER PERFORMANCE
MEASURING CLASSIFIER PERFORMANCE ERROR COUNTING Error types in a two-class problem False positives (type I error): True label is -1, predicted label is +1. False negative (type II error): True label is
Data Mining Classification: Bayesian Decision Theory
Data Mining Classification: Bayesian Decision Theory Lecture Notes for Chapter 2 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, 2nd ed. New York: Wiley, 2001. Lecture Notes for Chapter
DATA MINING OVERFITTING AND EVALUATION
DATA MINING OVERFITTING AND EVALUATION 1 Overfitting Will cover mechanisms for preventing overfitting in decision trees But some of the mechanisms and concepts will apply to other algorithms 2 Occam s
DATA MINING LECTURE 9. Classification Decision Trees Evaluation
DATA MINING LECTURE 9 Classification Decision Trees Evaluation 10 10 Illustrating Classification Task Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium
Network Traffic Measurements and Analysis
DEIB - Politecnico di Milano Fall, 2017 Sources Hastie, Tibshirani, Friedman: The Elements of Statistical Learning James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning Andrew Ng:
Part II: A broader view
Part II: A broader view Understanding ML metrics: isometrics, basic types of linear isometric plots linear metrics and equivalences between them skew-sensitivity non-linear metrics Model manipulation:
What is Learning? CS 343: Artificial Intelligence Machine Learning. Raymond J. Mooney. Problem Solving / Planning / Control.
What is Learning? CS 343: Artificial Intelligence Machine Learning Herbert Simon: Learning is any process by which a system improves performance from experience. What is the task? Classification Problem
Classification. Slide sources:
Classification Slide sources: Gideon Dror, Academic College of TA Yaffo Nathan Ifill, Leicester MA4102 Data Mining and Neural Networks Andrew Moore, CMU : http://www.cs.cmu.edu/~awm/tutorials 1 Outline
Data Mining and Knowledge Discovery Practice notes Numeric prediction and descriptive DM
Practice notes 4..9 Practice plan Data Mining and Knowledge Discovery Knowledge Discovery and Knowledge Management in e-science Petra Kralj Novak Petra.Kralj.Novak@ijs.si Practice, 9//4 9//: Predictive
Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining
Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data
Classification Algorithms in Data Mining
August 9th, 2016 Suhas Mallesh Yash Thakkar Ashok Choudhary CIS660 Data Mining and Big Data Processing -Dr. Sunnie S. Chung Classification Algorithms in Data Mining Deciding on the classification algorithms
Tutorials Case studies
1. Subject Three curves for the evaluation of supervised learning methods. Evaluation of classifiers is an important step of the supervised learning process. We want to measure the performance of the classifier.
Evaluating Machine Learning Methods: Part 1
Evaluating Machine Learning Methods: Part 1 CS 760@UW-Madison Goals for the lecture you should understand the following concepts bias of an estimator learning curves stratified sampling cross validation
CS 584 Data Mining. Classification 3
CS 584 Data Mining Classification 3 Today Model evaluation & related concepts Additional classifiers Naïve Bayes classifier Support Vector Machine Ensemble methods 2 Model Evaluation Metrics for Performance
Evaluating Machine-Learning Methods. Goals for the lecture
Evaluating Machine-Learning Methods Mark Craven and David Page Computer Sciences 760 Spring 2018 www.biostat.wisc.edu/~craven/cs760/ Some of the slides in these lectures have been adapted/borrowed from
Decision Tree CE-717 : Machine Learning Sharif University of Technology
Decision Tree CE-717 : Machine Learning Sharif University of Technology M. Soleymani Fall 2012 Some slides have been adapted from: Prof. Tom Mitchell Decision tree Approximating functions of usually discrete
Evaluation of different biological data and computational classification methods for use in protein interaction prediction.
Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Yanjun Qi, Ziv Bar-Joseph, Judith Klein-Seetharaman Protein 2006 Motivation Correctly
CISC 4631 Data Mining
CISC 4631 Data Mining Lecture 05: Overfitting Evaluation: accuracy, precision, recall, ROC Theses slides are based on the slides by Tan, Steinbach and Kumar (textbook authors) Eamonn Koegh (UC Riverside)
Information Management course
Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 20: 10/12/2015 Data Mining: Concepts and Techniques (3 rd ed.) Chapter
CS249: ADVANCED DATA MINING
CS249: ADVANCED DATA MINING Classification Evaluation and Practical Issues Instructor: Yizhou Sun yzsun@cs.ucla.edu April 24, 2017 Homework 2 out Announcements Due May 3 rd (11:59pm) Course project proposal
Evaluation Metrics. (Classifiers) CS229 Section Anand Avati
Evaluation Metrics (Classifiers) CS Section Anand Avati Topics Why? Binary classifiers Metrics Rank view Thresholding Confusion Matrix Point metrics: Accuracy, Precision, Recall / Sensitivity, Specificity,
CLASSIFICATION JELENA JOVANOVIĆ. Web:
CLASSIFICATION JELENA JOVANOVIĆ Email: jeljov@gmail.com Web: http://jelenajovanovic.net OUTLINE What is classification? Binary and multiclass classification Classification algorithms Naïve Bayes (NB) algorithm
Lecture Notes for Chapter 4
Classification - Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Slides by Tan, Steinbach, Kumar adapted by Michael Hahsler Look for accompanying R code on the course web
Machine Learning Techniques for Data Mining
Machine Learning Techniques for Data Mining Eibe Frank University of Waikato New Zealand 10/25/2000 1 PART V Credibility: Evaluating what s been learned 10/25/2000 2 Evaluation: the key to success How
MIT Samberg Center Cambridge, MA, USA. May 30 th June 2 nd, by C. Rea, R.S. Granetz MIT Plasma Science and Fusion Center, Cambridge, MA, USA
Exploratory Machine Learning studies for disruption prediction on DIII-D by C. Rea, R.S. Granetz MIT Plasma Science and Fusion Center, Cambridge, MA, USA Presented at the 2 nd IAEA Technical Meeting on
Evaluation. Evaluate what? For really large amounts of data... A: Use a validation set.
Evaluate what? Evaluation Charles Sutton Data Mining and Exploration Spring 2012 Do you want to evaluate a classifier or a learning algorithm? Do you want to predict accuracy or predict which one is better?
Data Mining D E C I S I O N T R E E. Matteo Golfarelli
Data Mining D E C I S I O N T R E E Matteo Golfarelli Decision Tree It is one of the most widely used classification techniques that allows you to represent a set of classification rules with a tree. Tree:
Metrics Overfitting Model Evaluation Research directions. Classification. Practical Issues. Huiping Cao. lassification-issues, Slide 1/57
lassification-issues, Slide 1/57 Classification Practical Issues Huiping Cao lassification-issues, Slide 2/57 Outline Criteria to evaluate a classifier Underfitting and overfitting Model evaluation lassification-issues,
Chapter 3: Supervised Learning
Chapter 3: Supervised Learning Road Map Basic concepts Evaluation of classifiers Classification using association rules Naïve Bayesian classification Naïve Bayes for text classification Summary 2 An example
Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4. Introduction to Data Mining
Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar (modified by Predrag Radivojac, 2017) Classification:
10 Classification: Evaluation
CSE4334/5334 Data Mining 10 Classification: Evaluation Chengkai Li Department of Computer Science and Engineering University of Texas at Arlington Fall 2018 (Slides courtesy of Pang-Ning Tan, Michael Steinbach
Machine Learning and Bioinformatics 機器學習與生物資訊學
Molecular Biomedical Informatics 分子生醫資訊實驗室 機器學習與生物資訊學 Machine Learning & Bioinformatics 1 Evaluation The key to success 2 Three datasets of which the answers must be known 3 Note on parameter tuning It
Statistics 202: Statistical Aspects of Data Mining
Statistics 202: Statistical Aspects of Data Mining Professor Rajan Patel Lecture 9 = More of Chapter 5 Agenda: 1) Lecture over more of Chapter 5 1 Introduction to Data Mining by Tan, Steinbach, Kumar Chapter
INTRODUCTION TO DATA MINING. Daniel Rodríguez, University of Alcalá
INTRODUCTION TO DATA MINING Daniel Rodríguez, University of Alcalá Outline Knowledge Discovery in Datasets Model Representation Types of models Supervised Unsupervised Evaluation (Acknowledgement: Jesús
Data Mining: Classifier Evaluation. CSCI-B490 Seminar in Computer Science (Data Mining)
Data Mining: Classifier Evaluation CSCI-B490 Seminar in Computer Science (Data Mining) Predictor Evaluation 1. Question: how good is our algorithm? how will we estimate its performance? 2. Question: what
Data Mining Concepts & Techniques
Data Mining Concepts & Techniques Lecture No. 03 Data Processing, Data Mining Naeem Ahmed Email: naeemmahoto@gmail.com Department of Software Engineering Mehran Univeristy of Engineering and Technology
Machine Learning Techniques for Data Mining
Machine Learning Techniques for Data Mining Eibe Frank University of Waikato New Zealand 10/25/2000 1 PART VII Moving on: Engineering the input and output 10/25/2000 2 Applying a learner is not all Already
Naïve Bayes Classification. Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others
Naïve Bayes Classification Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others Things We d Like to Do Spam Classification Given an email, predict
Classification. 1 o Semestre 2007/2008
Classification Departamento de Engenharia Informática Instituto Superior Técnico 1 o Semestre 2007/2008 Slides baseados nos slides oficiais do livro Mining the Web c Soumen Chakrabarti. Outline 1 2 3 Single-Class
Decision Trees Dr. G. Bharadwaja Kumar VIT Chennai
Decision Trees Decision Tree Decision Trees (DTs) are a nonparametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target
Slides for Data Mining by I. H. Witten and E. Frank
Slides for Data Mining by I. H. Witten and E. Frank 7 Engineering the input and output Attribute selection Scheme-independent, scheme-specific Attribute discretization Unsupervised, supervised, error-
CS570: Introduction to Data Mining
CS570: Introduction to Data Mining Classification Advanced Reading: Chapter 8.4 & 8.5 Han, Chapters 4.5 & 4.6 Tan Anca Doloc-Mihu, Ph.D. Slides courtesy of Li Xiong, Ph.D., 2011 Han, Kamber & Pei. Data
Data Mining. Covering algorithms. Covering approach At each stage you identify a rule that covers some of instances. Fig. 4.
Data Mining Chapter 4. Algorithms: The Basic Methods (Covering algorithm, Association rule, Linear models, Instance-based learning, Clustering) 1 Covering approach At each stage you identify a rule that
Louis Fourrier Fabien Gaie Thomas Rolf
CS 229 Stay Alert! The Ford Challenge Louis Fourrier Fabien Gaie Thomas Rolf Louis Fourrier Fabien Gaie Thomas Rolf 1. Problem description a. Goal Our final project is a recent Kaggle competition submitted
ECLT 5810 Evaluation of Classification Quality
ECLT 5810 Evaluation of Classification Quality Reference: Data Mining Practical Machine Learning Tools and Techniques, by I. Witten, E. Frank, and M. Hall, Morgan Kaufmann Testing and Error Error rate:
Algorithms: Decision Trees
Algorithms: Decision Trees A small dataset: Miles Per Gallon Suppose we want to predict MPG From the UCI repository A Decision Stump Recursion Step Records in which cylinders = 4 Records in which cylinders
Pattern recognition (4)
Pattern recognition (4) 1 Things we have discussed until now Statistical pattern recognition Building simple classifiers Supervised classification Minimum distance classifier Bayesian classifier (1D and
7. Decision or classification trees
7. Decision or classification trees Next we are going to consider a rather different approach from those presented so far to machine learning that use one of the most common and important data structure,
Classification Salvatore Orlando
Classification Salvatore Orlando 1 Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. The values of the
Homework 1 Sample Solution
Homework 1 Sample Solution 1. Iris: All attributes of iris are numeric, therefore ID3 of weka cannt be applied to this data set. Contact-lenses: tear-prod-rate = reduced: none tear-prod-rate = normal astigmatism
Best First and Greedy Search Based CFS and Naïve Bayes Algorithms for Hepatitis Diagnosis
Best First and Greedy Search Based CFS and Naïve Bayes Algorithms for Hepatitis Diagnosis CHAPTER 3 BEST FIRST AND GREEDY SEARCH BASED CFS AND NAÏVE BAYES ALGORITHMS FOR HEPATITIS DIAGNOSIS 3.1 Introduction
Lecture 25: Review I
Lecture 25: Review I Reading: Up to chapter 5 in ISLR. STATS 202: Data mining and analysis Jonathan Taylor 1 / 18 Unsupervised learning In unsupervised learning, all the variables are on equal standing,
Logistic Regression: Probabilistic Interpretation
Logistic Regression: Probabilistic Interpretation Approximate 0/1 Loss Logistic Regression Adaboost (z) SVM Solution: Approximate 0/1 loss with convex loss ( surrogate loss) 0-1 z = y w x SVM (hinge),
CSC411/2515 Tutorial: K-NN and Decision Tree
CSC411/2515 Tutorial: K-NN and Decision Tree Mengye Ren csc{411,2515}ta@cs.toronto.edu September 25, 2016 Cross-validation K-nearest-neighbours Decision Trees Review: Motivation for Validation Framework:
Data Mining. Practical Machine Learning Tools and Techniques. Slides for Chapter 5 of Data Mining by I. H. Witten, E. Frank and M. A.
Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 5 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Credibility: Evaluating what s been learned Issues: training, testing,
Instance-Based Representations. k-nearest Neighbor. k-nearest Neighbor. k-nearest Neighbor. exemplars + distance measure. Challenges.
Instance-Based Representations exemplars + distance measure Challenges. algorithm: IB1 classify based on majority class of k nearest neighbors learned structure is not explicitly represented choosing k
Predictive Analysis: Evaluation and Experimentation. Heejun Kim
Predictive Analysis: Evaluation and Experimentation Heejun Kim June 19, 2018 Evaluation and Experimentation Evaluation Metrics Cross-Validation Significance Tests Evaluation Predictive analysis: training
Ensemble Methods, Decision Trees
CS 1675: Intro to Machine Learning Ensemble Methods, Decision Trees Prof. Adriana Kovashka University of Pittsburgh November 13, 2018 Plan for This Lecture Ensemble methods: introduction Boosting Algorithm
CPSC 340: Machine Learning and Data Mining. Non-Parametric Models Fall 2016
CPSC 340: Machine Learning and Data Mining Non-Parametric Models Fall 2016 Assignment 0: Admin 1 late day to hand it in tonight, 2 late days for Wednesday. Assignment 1 is out: Due Friday of next week.
Part I. Classification & Decision Trees. Classification. Classification. Week 4 Based in part on slides from textbook, slides of Susan Holmes
Week 4 Based in part on slides from textbook, slides of Susan Holmes Part I Classification & Decision Trees October 19, 2012 1 / 1 2 / 1 Classification Classification Problem description We are given a
Naïve Bayes Classification. Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others
Naïve Bayes Classification Material borrowed from Jonathan Huang and I. H. Witten s and E. Frank s Data Mining and Jeremy Wyatt and others Things We d Like to Do Spam Classification Given an email, predict
Introduction to Automated Text Analysis. bit.ly/poir599
Introduction to Automated Text Analysis Pablo Barberá School of International Relations University of Southern California pablobarbera.com Lecture materials: bit.ly/poir599 Today 1. Solutions for last
Model s Performance Measures
Model s Performance Measures Evaluating the performance of a classifier Section 4.5 of course book. Taking into account misclassification costs Class imbalance problem Section 5.7 of course book. TNM033:
Partitioning Data. IRDS: Evaluation, Debugging, and Diagnostics. Cross-Validation. Cross-Validation for parameter tuning
Partitioning Data IRDS: Evaluation, Debugging, and Diagnostics Charles Sutton University of Edinburgh Training Validation Test Training : Running learning algorithms Validation : Tuning parameters of learning
10601 Machine Learning. Model and feature selection
10601 Machine Learning Model and feature selection Model selection issues We have seen some of this before Selecting features (or basis functions) Logistic regression SVMs Selecting parameter value Prior
Nearest neighbor classification DSE 220
Nearest neighbor classification DSE 220 Decision Trees Target variable Label Dependent variable Output space Person ID Age Gender Income Balance Mortgag e payment 123213 32 F 25000 32000 Y 17824 49 M 12000-3000
Machine Learning: Symbolische Ansätze
Machine Learning: Symbolische Ansätze Evaluation and Cost-Sensitive Learning Evaluation Hold-out Estimates Cross-validation Significance Testing Sign test ROC Analysis Cost-Sensitive Evaluation ROC space
Input: Concepts, Instances, Attributes
Input: Concepts, Instances, Attributes 1 Terminology Components of the input: Concepts: kinds of things that can be learned aim: intelligible and operational concept description Instances: the individual,
CSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Classification (Basic Concepts) Huan Sun, CSE@The Ohio State University 09/12/2017 Slides adapted from UIUC CS412, Fall 2017, by Prof. Jiawei Han Classification: Basic Concepts
Chuck Cartledge, PhD. 23 September 2017
Introduction K-Nearest Neighbors Na ıve Bayes Hands-on Q&A Conclusion References Files Misc. Big Data: Data Analysis Boot Camp Classification with K-Nearest Neighbors and Na ıve Bayes Chuck Cartledge,
CS178: Machine Learning and Data Mining. Complexity & Nearest Neighbor Methods
+ CS78: Machine Learning and Data Mining Complexity & Nearest Neighbor Methods Prof. Erik Sudderth Some materials courtesy Alex Ihler & Sameer Singh Machine Learning Complexity and Overfitting Nearest
CCRMA MIR Workshop 2014 Evaluating Information Retrieval Systems. Leigh M. Smith Humtap Inc.
CCRMA MIR Workshop 2014 Evaluating Information Retrieval Systems Leigh M. Smith Humtap Inc. leigh@humtap.com Basic system overview Segmentation (Frames, Onsets, Beats, Bars, Chord Changes, etc) Feature
Nächste Woche. Dienstag, : Vortrag Ian Witten (statt Vorlesung) Donnerstag, 4.12.: Übung (keine Vorlesung) IGD, 10h. 1 J.
1 J. Fürnkranz Nächste Woche Dienstag, 2. 12.: Vortrag Ian Witten (statt Vorlesung) IGD, 10h 4 Donnerstag, 4.12.: Übung (keine Vorlesung) 2 J. Fürnkranz Evaluation and Cost-Sensitive Learning Evaluation
Evaluating Classifiers
Evaluating Classifiers Charles Elkan elkan@cs.ucsd.edu January 18, 2011 In a real-world application of supervised learning, we have a training set of examples with labels, and a test set of examples with
CSE Data Mining Concepts and Techniques STATISTICAL METHODS (REGRESSION) Professor- Anita Wasilewska. Team 13
CSE 634 - Data Mining Concepts and Techniques STATISTICAL METHODS Professor- Anita Wasilewska (REGRESSION) Team 13 Contents Linear Regression Logistic Regression Bias and Variance in Regression Model Fit
Lecture 7: Decision Trees
Lecture 7: Decision Trees Instructor: Outline 1 Geometric Perspective of Classification 2 Decision Trees Geometric Perspective of Classification Perspective of Classification Algorithmic Geometric Probabilistic...
INTRODUCTION TO MACHINE LEARNING. Measuring model performance or error
INTRODUCTION TO MACHINE LEARNING Measuring model performance or error Is our model any good? Context of task Accuracy Computation time Interpretability 3 types of tasks Classification Regression Clustering