Learning is a very general term denoting the way in which agents: We discuss 2 resampling methods in this chapter - cross-validation - the bootstrap, Universit de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr, An Introduction to Data Mining. We can plot precision & recall vs threshold to get information about how their value changes according to the threshold. We studied classification model evaluation & talked about multiple evaluation metrics. Dr. Thomas Jensen Expedia.com, Machine Learning. Before starting out directly with classification lets talk about ML tasks in general. For example, in the case of a cancer detection system, youll prefer having high recall & low precision. ## dummy example But opting out of some of these cookies may affect your browsing experience. ML4Bio 2012 February 17 th, 2012 Quaid Morris, Comparison of Data Mining Techniques used for Financial Data Analysis, III. 1) Correct Target labels What is Data Mining? One random split is used for really large data For medium sized repeated hold-out Holdout estimate can be made more reliable by repeating the process with different subsamples In each iteration, a certain proportion is randomly selected for training (possibly with stratification) The error rates (classification accuracies) on the different iterations are averaged to yield an overall error rate Calculate also a standard deviation! -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to, RESEARCH Open Access On the application of multi-class classification in physical therapy recommendation Jing Zhang 1,PengCao 1,DouglasPGross 2 and Osmar R Zaiane 1* Abstract Recommending optimal rehabilitation, Neural and Evolutionary Computing. The stacking ensemble approach, Overview. A Machine-Learning Approach to Discovering Company Home Pages Wojciech Gryc Oxford Internet Institute University of Oxford Oxford, UK OX1 3JS Email: wojciech.gryc@oii.ox.ac.uk Prem Melville IBM T.J. Watson, CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Gney, Kbra Kalkan 1/15/2013 Keywords: Non-linear, Brochure More information from http://www.researchandmarkets.com/reports/2170926/ Data Mining for Business Intelligence. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. The function takes 2 required parameters This is called Precision/Recall Trade-off. There will be a long explanation on this topic in future lectures. Problem: variance in estimate. Given a set of pre-classified examples, discover the classification knowledge representation, to be used either as a classifier to classify new cases (a predictive perspective) or to describe classification situations in data (a descriptive perspective). 27 Leave-One-Out cross-validation Leave-One-Out: a particular form of cross-validation: Set number of folds to number of training instances i.e., for n training instances, build classifier n times but from n -1 training examples Makes best use of the data. Quite computationally expensive! Test and Validation Set. The probability that the algorithm will output a successful hypothesis. Practical Data Science with Azure Machine Learning, SQL Data Mining, and R, Comparison of machine learning methods for intelligent tutoring systems, Data Mining Techniques for Prognosis in Pancreatic Cancer, A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier, Gerry Hobbs, Department of Statistics, West Virginia University, Data Mining Algorithms Part 1. Notify me of follow-up comments by email. Read more in T.Mitchell s book chapter 7. or P.Cichosz (Polish) coursebook Systemy uczce si. Hall Issues: training, testing, Introduction to Machine Learning Lecture 1 Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction Logistics Prerequisites: basics concepts needed in probability and statistics. We will come back to it latter during the lecture on pruning structures of classifiers. FAQs CS535 BIG DATA W6.B.3. Unsupervised Learning In unsupervised learning, the model by itself tries to identify patterns in the training set. from sklearn.metrics import confusion_matrix How can we evaluate a model Test the model Statistical tests Considerations in evaluating a Model, Knowledge Discovery and Data Mining Unit # 10 Sajjad Haider Fall 2012 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right, Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15, Knowledge Discovery and Data Mining Unit # 11 Sajjad Haider Fall 2013 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right, Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers, 82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. As you can see as the threshold increases precision increases but at the cost of recall. It is mandatory to procure user consent prior to running these cookies on your website. Any good classifier should be as far as possible from the straight line passing through (0,0) & (1,1). Can be used, Comp. Precision = TP/(TP+FP), Precision can be generated easily using precision_score() function from sklearn library. As in the graph above, SGD & random forest models are compared. Big Data World. How could you evaluate the classification knowledge: Evaluation measures predictive ability. Three types of messages: A, B, C. Assume A is the oldest type, and C is the most recent type. One of the classic techniques used, Statistical Learning: Chapter 5 Resampling methods (Cross-validation and bootstrap) (Note: prior to these notes, we'll discuss a modification of an earlier train/test experiment from Ch 2) We discuss 2, Universit de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr WEKA Gallirallus Zeland) australis : Endemic bird (New Characteristics Waikato university Weka is a collection, Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous, D-optimal plans in observational studies Constanze Pumpln Stefan Rping Katharina Morik Claus Weihs October 11, 2005 Abstract This paper investigates the use of Design of Experiments in observational, An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content, Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms k-means Hierarchical Main, Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 2015-03-05 Roman Kern (KTI, TU Graz) Ensemble Methods 2015-03-05 1 / 38 Outline 1 Introduction 2 Classification, Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania, Summary Data Mining & Process Mining (1BM46) Made by S.P.T. Dan Steinberg and N. Scott Cardell, FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN-BASED KNOWLEDGE-DISCOVERY PROCESS, Getting Even More Out of Ensemble Selection, E-commerce Transaction Anomaly Classification, Predicting Student Performance by Using Data Mining Methods for Classification, F. Aiolli - Sistemi Informativi 2007/2008, Event driven trading new studies on innovative way. Overfitting fitting the training data too precisely - usually leads to poor results on new data. Cross-validation for detecting and preventing overfitting Note to other teachers and users of these slides. Do not learn too much peculiarities in training data; think about generality abilities! 5 cv are also popular. Supervised learning task mainly consists of Regression & Classification. There is another classification metric that is a combination of both Recall & Precision. Recall is defined as the ratio of True Positives count to the total Actual Positive count. Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. 2) Predicted Target labels. Data, Measurements, Features Middle East Technical University Dep. CS 2750 Machine Learning. STATISTICA Formula Guide: Logistic Regression. Introduction to Evaluation of Classification Model. Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not. Andrew would be delighted if ou found this source material useful in giving our own lectures. Can one characterize the number of mistakes that an algorithm will make during learning? Necessary cookies are absolutely essential for the website to function properly. Accuracy is the ratio of correct prediction count by total predictions made. But just for the sake of some revision lets briefly discuss it. In the above graph, you can observe that the Random Forest model is working better compared to SGD. Chapter 18, 21. 1) Correct Target labels The harmonic mean is more sensitive to low values, so the F1 will be high only when both precision & recall are high. 1) Correct Target labels TPR means recall. 15 Experimental evaluation of classifiers How predictive is the model we learned? Reinforcement Learning This is an altogether different type. 6 Evaluation criteria (1) Predictive (Classification) accuracy: this refers to the ability of the model to correctly predict the class label of new or previously unseen data: accuracy = % of testing set examples correctly classified by the classifier Speed: this refers to the computation costs involved in generating and using the model Robustness: this is the ability of the model to make correct predictions given noisy data or data with missing values, 7 Evaluation criteria (2) Scalability: this refers to the ability to construct the model efficiently given large amount of data Interpretability: this refers to the level of understanding and insight that is provided by the model Simplicity: decision tree size rule compactness Domain-dependent quality indicators, 8 Predictive accuracy / error General view (statistical learning point of view): Lack of generalization prediction risk: R( f ) = E L( y, f ( x)) xy where L( y, y) is a loss or cost of predicting value when the actual value is y and E is expected value over the joint distribution of all (x,y) for data to be predicted. From this graph, one can pick a suitable threshold as per their requirements. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk, Mining the Software Change Repository of a Legacy Telephony System, THE HYBRID CART-LOGIT MODEL IN CLASSIFICATION AND DATA MINING. Machine Learning in Spam Filtering A Crash Course in ML Konstantin Tretyakov kt@ut.ee Institute of Computer Science, University of Tartu Overview Spam is Evil ML for Spam Filtering: General Idea, Problems. Related Fields and Disciplines. FPR is the ratio of Negative classes inaccurately being classified as positive. 12 Other measures for performance evaluation Classifiers: Misclassification cost Lift Brier score, information score, margin class probabilities Sensitivity and specificity measures (binary problems), ROC curve AUC analysis. Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses, Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C. What is learning? For simplicity purposes, we assume a classifier which outputs whether the input alphabet is A or not. SLIQ: Sort the values, Class #6: Non-linear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Non-linear classification Linear Support Vector Machines, Comparison of Data Mining Techniques used for Financial Data Analysis Abhijit A. Sawant 1, P. M. Chawan 2 1 Student, 2 Associate Professor, Department of Computer Technology, VJTI, Mumbai, INDIA Abstract. These cookies do not store any personal information. more about comparing many algorithms and ROC analysis will be presented during future lectures), Evaluation & Validation: Credibility: Evaluating what has been learned How predictive is a learned model? 2) Predicted Target labels. We need to use precision along with another metric called Recall. It is called the F1 score. Training the Matching Model, Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data, Data Mining for Business Intelligence. 22 Remarks on hold-out It is important that the test data is not used in any way to create the classifier! Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. 2 Outline 1. We will take a tiny section of the confusion matrix above for a better understanding. Lecture 19 - Bagging. Made by S.P.T. Error on the training data is not a good indicator of performance on future data Q: Why? Note: this is domain dependent! Learning denotes changes in a system that enable a system to do the same task more efficiently the next, Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. A confusion matrix is a n x n matrix (where n is the number of labels) used to describe the performance of a classification model. Is it always probably approximate correct? Hall Combining multiple models Bagging The basic idea, Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods Jerzy B laszczyski 1, Krzysztof Dembczyski 1, Wojciech Kot lowski 1, and Mariusz Paw lowski 2 1 Institute of Computing, Performance Metrics for Graph Mining Tasks 1 Outline Introduction to Performance Metrics Supervised Learning Performance Metrics Unsupervised Learning Performance Metrics Optimizing Metrics Statistical, Feature vs. Classifier Fusion for Predictive Data Mining a Case Study in Pesticide Classification Henrik Bostrm School of Humanities and Informatics University of Skvde P.O. Data Mining Essentials, Chapter 6. Best of all: Cross Validation Dr. Thomas Jensen Expedia.com About Me PhD from ETH Used to be a statistician at Link, now Senior Business Analyst at Expedia Manage a database with 720,000 Hotels that are not on contract, Machine Learning Javier Bjar cbea LSI - FIB Term 2012/2013 Javier Bjar cbea (LSI - FIB) Machine Learning Term 2012/2013 1 / 34 Outline 1 Introduction to Inductive learning 2 Search and inductive learning, Chronological Sampling for Email Filtering Ching-Lung Fu 2, Daniel Silver 1, and James Blustein 2 1 Acadia University, Wolfville, Nova Scotia, Canada 2 Dalhousie University, Halifax, Nova Scotia, Canada, Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. This trained model can be later used to predict output for any unknown input. All examples available or incremental / active approaches? Also the standard deviation is essential for comparing learning algorithms. 29 Comparing data mining algorithms Frequent situation: we want to know which one of two learning schemes performs better. 30 Comparing two classifiers on the same data Summary of results in separate folds Podzia Kl_1 Kl_2 1 87,45 88,4 2 86,5 88,1 3 86,4 87,2 4 86, ,8 87,6 6 86,6 86,4 7 87, ,2 87, ,8 87,2 Srednia 86,98 87,43 Odchylenie 0,65 0,85 The general question: given two classifiers K1 and K2 produced by feeding a training dataset D to two algorithms A1 and A2, which classifier will be more accurate in classifying new examples? Precision in itself will not be enough as a model can make just one correct positive prediction & return the rest as negative. In reality, there is no ideal recall or precision. Other combination techniques like voting, bagging etc are also described, Overview Evaluation Connectionist and Statistical Language Processing Frank Keller keller@coli.uni-sb.de Computerlinguistik Universitt des Saarlandes training set, validation set, test set holdout, stratification, Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Table of Contents. 19 Step 1: Split data into train and test sets Historical data Results Known Training set Data + Testing set, 20 Step 2: Build a model on a training set THE PAST Results Known Training set Data + Model Builder Testing set, 21 Step 3: Evaluate on test set Results Known Training set Data + Testing set Model Builder Y N Evaluate Predictions. Precision and recall, F-measure.

1 Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010. The function takes 2 required parameters Better not to talk about it. Train classifier model, training & test set are provided to you. It is the harmonic mean of recall & precision. The rule of a supervisor? .

The function takes 2 required parameters 11 Confusion matrix and cost sensitive analysis Predicted Original classes K 1 K 2 K 3 K K K C( ) = r r = = i j 1 1 n ij c ij Costs assigned to different types of errors. Santosh Tirunagari : 245577, Cross-validation for detecting and preventing overfitting, Introduction to Machine Learning and Data Mining. Can we prevent overlapping? of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? Bootstrap review. If the model predicts A as an A, then the case is called, If the model predicts A a Not A, then the case is called, If the model predicts Not A as an A, then the case is called, If the model predicts Not A as a Not A, then the case is called. 18 Evaluation on LARGE data, hold-out A simple evaluation is sufficient Randomly split data into training and test sets (usually 2/3 for train, 1/3 for test) Build a classifier using the train set and evaluate it using the test set. Classification problems The main aim of a classification, Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? Machine Learning. 2. 2) Predicted Target labels. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes, Predict Influencers in the Social Network Ruishan Liu, Yang Zhao and Liuyu Zhou Email: rliu2, yzhao2, lyzhou@stanford.edu Department of Electrical Engineering, Stanford University Abstract Given two persons, Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification Tina R. Patil, Mrs. S. S. Sherekar Sant Gadgebaba Amravati University, Amravati tnpatil2@gmail.com, ss_sherekar@rediffmail.com. A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier G.T.

Another easy way of remembering this is by referring to the below diagram. 2. 7.1 Introduction, Decompose Error Rate into components, some of which can be measured on unlabeled data, A Property & Casualty Insurance Predictive Modeling Process in SAS, BIDM Project. These cookies will be stored in your browser only with your consent. This website uses cookies to improve your experience while you navigate through the website. 32 An example of paired t-test = 0,05 One classifier (Single MODLEM) versus other bagging schema - J.Stefanowski, 33 Other sampling techniques for classifiers There are other approaches to learn classifiers: Incremental learning Batch learning Windowing Active learning Some of them evaluate classification abilities in stepwise way: Various forms of learning curves, 34 An example of a learning curve Used nave Bayes model for text classification in a Bayesian learning setting (20 Newsgroups dataset) - [McCallum & Nigam, 1998], 35 Summary What is the classification task? So the precision will be 1/(1+0)=1. This can help you to pick a sweet spot for your model. Mining the Software Change Repository of a Legacy Telephony System Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin School of Information Technology and Engineering University of Ottawa, Ottawa, THE HYBID CAT-LOGIT MODEL IN CLASSIFICATION AND DATA MINING Introduction Dan Steinberg and N. Scott Cardell Most data-mining projects involve classification problems assigning objects to classes whether, Performance Measures in Data Mining Common Performance Measures used in Data Mining and Machine Learning Approaches L. Richter J.M. Supervised Learning In Supervised learning, the model is first trained using a Training set(it contains input-expected output pairs).