# Microsoft Azure Machine learning Algorithms

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Microsoft Azure Machine learning Algorithms Tomaž

3 Speaker info https://tomaztsql.wordpress.com

4 Agenda Focus on explanation of algorithms available for predictive analytics in Azure Machine Learning service. Algorithms 1) regression algorithms, 2) Two-Class classifications, 3) Multi-class classification, 4) Clustering - Explore algorithm - which algorithm is used and useful for what kind of empirical problem - which is suitable for particular data-set.

5 Before start... Why this session?!

6 First things first

7 First things something By Type Categorical Numerical Nominal Ordinal Interval Ratio gender (male/ Female), eye colour (blue, green, black,...), marital status (S, M, D,V,...), Buyer Nonbuyer (0 1) No inherited order; all values are the same and all values equal in representation education (primary, secondary, high school,...), Salary (<101, , ,..) Ordering can be applied; values can be compared with =,>,< and recoded with numbers / classes. Height (173cm, 196cm), Invoice Value (24.4, 42.6,..) Ordering can be applied; values can be compared with =,>,< and expressed 2 is 2x times bigger.. Temperature: Celsius vs. Fahrenheit

8 For warmup...regression

9 First things second

10 Sample Data

11 1 General (interferance) statistics Apply Math Operation -> Applies a mathematical operation to column values Compute Elementary Statistics -> Calculates specified summary statistics for selected dataset columns Compute Linear Correlation -> Calculates the linear correlation between column values in a dataset Descriptive Statistics -> Generates a basic descriptive statistics report for the columns in a dataset Evaluate Probability Function -> Fits a specified probability distribution function to a dataset Replace Discrete Values -> Replaces discrete values from one column with numeric values based on another column Test Hypothesis Using t-test -> Compares means from two datasets using a t-test Source: https://msdn.microsoft.com/en-us/library/azure/dn aspx

12 1 General Statistics

13 1 Spliting data Splitting Mode: - Split rows - Recommender split - Regular / Relative expression Random seed Stratified split it depends on the size of our dataset. If it is large enough, 66% split is a good choice (66% for training and the others for test). if it is a moderated dataset, 10-fold cross validation (or leave-one-out) can be a good choice. if your dataset is small then Bootstraping is good. Jackknife, cross validation, n-fold cross validation,...

14 1 Spliting data

15 1 Initializing Model

16 1 Initializing Model

17 1 Initializing Model Making list of algorithms more transparent

18 Algorithms in Theory Regression Linear and Logistic Azure ML: Linear Regression Azure ML: Two-class Classification Logistic Regression Multiclass classification Logistic Regression

19 Algorithms in Theory Decision Tree, Decision Forests, Decision Jungles Decision tree Decision Forest Decision Jungle Azure ML: Regression boosted decision tree Two-class classification boosted decision tree Azure ML: Regression decision forrest Two-class classification decision forrest Multi classification decision forrest Azure ML: Multi-class decision jungle Two-class classification decision jungle

20 Algorithms in Theory Naive Bayes Azure ML: Regression Bayes linear Two Class classification Bayes point machine

21 Algorithms in Theory Neural networks and perceptrons Azure ML: Regression Neural networks Two Class classification Neural networks Multi Class classification Neural networks Two Class Classification averaged perceptrons

22 Algorithms in Theory SVM Azure ML: Two Class classification SVM Two Class classification locally deep SVM Anomaly detection SVM

23 1 Regression Algorithms

24 1 Regression Algorithms Parameters

25 1 Regression Algorithms

26 Evaluating Regression Algorithms Metrics to measure how close predictions are to eventual outcomes Metrics of differences between predicted values and actual values. Summarization of regression model how well fits a statistical model; R^2 = 1 model is perfect, respectively

27 Evaluating Regression Algorithms Predicting Salary

28 Comparison of Regression Algorithms Regression Algorithm Accuracy Training time Linearity Customization Predicting Variable Type of independant variable(s) Data Quantity linear Good Fast Excellent Good Interval Any small to big Bayesian linear Good Fast Excellent Moderate Interval Any big decision forest Excellent Moderate Good Good Interval Any boosted decision tree Excellent Fast Good Good Interval Any big fast forest quantile Excellent Moderate Moderate Excellent Distribution (Interval) Any neural network Excellent Slow Moderate Excellent Interval Any smaller Excellent* Poisson Good Moderate (log linear) Good Interval (counts) Any small to big ordinal Good Moderate Excellent None Ordinal (order) Any small to big Scale: Excellent Good Moderate Fast Moderate Slow

29 2 Two-class Classification

30 2 Two-class Classification parameters

31 Regularization weight

32 2 Two-class Classification

33 2 Evaluating two-class Classification AUC/ROC: <= WTF?

34 2 Evaluating two-class Classification

35 Comparison of Two-class Classification Algorithms Two-class classification Accuracy Training time Linearity Customization Predicting Variable Type of independant variable(s) Data Quantity logistic regression Good Fast Excellent Good dichotomous / binary Any small-big decision forest Excellent Moderate Good Good dichotomous / binary Any small-big decision jungle Excellent Moderate Good Good dichotomous / binary Any big boosted decision tree Excellent Moderate Good Good dichotomous / binary Any big neural network Excellent Slow Moderate Excellent dichotomous / binary Any averaged perceptron Good Moderate Excellent Moderate dichotomous / binary Any support vector machine Excellent Moderate Excellent Good dichotomous / binary Any big locally deep support vector machine Good Slow Good Excellent dichotomous / binary Any big Bayes point machine Moderate Moderate Excellent Moderate dichotomous / binary Any Scale: Excellent Good Moderate Fast Moderate Slow

36 3 Multi-class Classification

37 3 Multi-class Classification parameters

38 3 Multi-class Classification

39 3 Evaluating multi-class Classification

40 3 Evaluating multi-class Classification

41 3 Comparison of Multi-class Classification Multi-class classification Accuracy Training time Linearity Customization Predicting Variable Type of independant variable(s) Data Quantity logistic regression Good Fast Excellent Good Nominal / ordinal (with 2+ classes) any small-big decision forest Excellent Moderate Good Good Nominal / ordinal (with 2+ classes) any big decision jungle Excellent Moderate Good Good Nominal / ordinal (with 2+ classes) any big neural network Excellent Slow Moderate Excellent Nominal / ordinal (with 2+ classes) any small Scale: Excellent Good Moderate Fast Moderate Slow

42 4 Using Sweeping and SMOTE

43 4 Using Sweeping and SMOTE

44 5 Clustering

45 5 Clustering

46 5 Clustering

47 5 Evaluating Clustering

48 Key takeaways https://azure.microsoft.com/en-us/documentation/articles/machinelearning-algorithm-cheat-sheet/ https://msdn.microsoft.com/enus/library/azure/dn aspx

49 How did you like it? Please give feedback to the event: to me as a speaker: sessionevaluation.aspx

50 THANKS!

### Benefits of analytics using Microsoft Azure Machine Learning (ML) Tomaž

Benefits of analytics using Microsoft Azure Machine Learning (ML) Tomaž Kaštrun @tomaz_tsql Gold sponsors Platinum sponsor Special Thanks Special Thanks to SQL Saturday Bratislava Organizers! Making SQL

### Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

### DATA ANALYTICS USING R

DATA ANALYTICS USING R Duration: 90 Hours Intended audience and scope: The course is targeted at fresh engineers, practicing engineers and scientists who are interested in learning and understanding data

### Social Media Mining. Data Mining Essentials

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

### Practical Data Science with Azure Machine Learning, SQL Data Mining, and R

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. Some of the topics will be

### MHI3000 Big Data Analytics for Health Care Final Project Report

MHI3000 Big Data Analytics for Health Care Final Project Report Zhongtian Fred Qiu (1002274530) http://gallery.azureml.net/details/81ddb2ab137046d4925584b5095ec7aa 1. Data pre-processing The data given

### Introduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu

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

### EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER ANALYTICS LIFECYCLE Evaluate & Monitor Model Formulate Problem Data Preparation Deploy Model Data Exploration Validate Models

### COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments

Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for

### Decision tree algorithm short Weka tutorial

Decision tree algorithm short Weka tutorial Croce Danilo, Roberto Basili Machine leanring for Web Mining a.a. 2009-2010 Machine Learning: brief summary Example You need to write a program that: given a

### COURSE SYLLABUS COURSE TITLE:

1 COURSE SYLLABUS COURSE TITLE: FORMAT: CERTIFICATION EXAMS: 55040 Data Mining: Predictive Analytics with Microsoft SQL Server Analysis Services and Excel Using PowerPivot and the Data Mining Add-Ins Instructor-Led

### Framing Business Problems as Data Mining Problems

Framing Business Problems as Data Mining Problems Asoka Diggs Data Scientist, Intel IT January 21, 2016 Legal Notices This presentation is for informational purposes only. INTEL MAKES NO WARRANTIES, EXPRESS

### 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

### Nominal and ordinal logistic regression

Nominal and ordinal logistic regression April 26 Nominal and ordinal logistic regression Our goal for today is to briefly go over ways to extend the logistic regression model to the case where the outcome

### Data Mining. Nonlinear Classification

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

### BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376

Course Director: Dr. Kayvan Najarian (DCM&B, kayvan@umich.edu) Lectures: Labs: Mondays and Wednesdays 9:00 AM -10:30 AM Rm. 2065 Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm.

### In this presentation, you will be introduced to data mining and the relationship with meaningful use.

In this presentation, you will be introduced to data mining and the relationship with meaningful use. Data mining refers to the art and science of intelligent data analysis. It is the application of machine

### Data Types. 1. Continuous 2. Discrete quantitative 3. Ordinal 4. Nominal. Figure 1

Data Types By Tanya Hoskin, a statistician in the Mayo Clinic Department of Health Sciences Research who provides consultations through the Mayo Clinic CTSA BERD Resource. Don t let the title scare you.

### Data Mining Algorithms Part 1. Dejan Sarka

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

### Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr

Université 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

### BIDM Project. Predicting the contract type for IT/ITES outsourcing contracts

BIDM Project Predicting the contract type for IT/ITES outsourcing contracts N a n d i n i G o v i n d a r a j a n ( 6 1 2 1 0 5 5 6 ) The authors believe that data modelling can be used to predict if an

### Lecture 6 - Data Mining Processes

Lecture 6 - Data Mining Processes Dr. Songsri Tangsripairoj Dr.Benjarath Pupacdi Faculty of ICT, Mahidol University 1 Cross-Industry Standard Process for Data Mining (CRISP-DM) Example Application: Telephone

### Data Mining - Evaluation of Classifiers

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

### Machine learning for algo trading

Machine learning for algo trading An introduction for nonmathematicians Dr. Aly Kassam Overview High level introduction to machine learning A machine learning bestiary What has all this got to do with

### BOOSTED REGRESSION TREES: A MODERN WAY TO ENHANCE ACTUARIAL MODELLING

BOOSTED REGRESSION TREES: A MODERN WAY TO ENHANCE ACTUARIAL MODELLING Xavier Conort xavier.conort@gear-analytics.com Session Number: TBR14 Insurance has always been a data business The industry has successfully

### Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05

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

### Statistics Graduate Courses

Statistics Graduate Courses STAT 7002--Topics in Statistics-Biological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.

### LCs for Binary Classification

Linear Classifiers A linear classifier is a classifier such that classification is performed by a dot product beteen the to vectors representing the document and the category, respectively. Therefore it

### MS1b Statistical Data Mining

MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to

### Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

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

### An Introduction to Data Mining

An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail

### Pentaho Data Mining Last Modified on January 22, 2007

Pentaho Data Mining Copyright 2007 Pentaho Corporation. Redistribution permitted. All trademarks are the property of their respective owners. For the latest information, please visit our web site at www.pentaho.org

### Car Insurance. Prvák, Tomi, Havri

Car Insurance Prvák, Tomi, Havri Sumo report - expectations Sumo report - reality Bc. Jan Tomášek Deeper look into data set Column approach Reminder What the hell is this competition about??? Attributes

### Methods for Interaction Detection in Predictive Modeling Using SAS Doug Thompson, PhD, Blue Cross Blue Shield of IL, NM, OK & TX, Chicago, IL

Paper SA01-2012 Methods for Interaction Detection in Predictive Modeling Using SAS Doug Thompson, PhD, Blue Cross Blue Shield of IL, NM, OK & TX, Chicago, IL ABSTRACT Analysts typically consider combinations

### Employer Health Insurance Premium Prediction Elliott Lui

Employer Health Insurance Premium Prediction Elliott Lui 1 Introduction The US spends 15.2% of its GDP on health care, more than any other country, and the cost of health insurance is rising faster than

### Content DESCRIPTIVE STATISTICS. Data & Statistic. Statistics. Example: DATA VS. STATISTIC VS. STATISTICS

Content DESCRIPTIVE STATISTICS Dr Najib Majdi bin Yaacob MD, MPH, DrPH (Epidemiology) USM Unit of Biostatistics & Research Methodology School of Medical Sciences Universiti Sains Malaysia. Introduction

### 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 Performance measures

### Decision Trees from large Databases: SLIQ

Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? If the training set does not fit into main memory, swapping makes C4.5 unpractical! SLIQ: Sort the values

### Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD

Predictive Analytics Techniques: What to Use For Your Big Data March 26, 2014 Fern Halper, PhD Presenter Proven Performance Since 1995 TDWI helps business and IT professionals gain insight about data warehousing,

### A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier

A Study Of Bagging And Boosting Approaches To Develop Meta-Classifier G.T. Prasanna Kumari Associate Professor, Dept of Computer Science and Engineering, Gokula Krishna College of Engg, Sullurpet-524121,

### Prerequisites. Course Outline

MS-55040: Data Mining, Predictive Analytics with Microsoft Analysis Services and Excel PowerPivot Description This three-day instructor-led course will introduce the students to the concepts of data mining,

### Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris

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

### PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

### When to Use a Particular Statistical Test

When to Use a Particular Statistical Test Central Tendency Univariate Descriptive Mode the most commonly occurring value 6 people with ages 21, 22, 21, 23, 19, 21 - mode = 21 Median the center value the

### Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal

Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether

### HT2015: SC4 Statistical Data Mining and Machine Learning

HT2015: SC4 Statistical Data Mining and Machine Learning Dino Sejdinovic Department of Statistics Oxford http://www.stats.ox.ac.uk/~sejdinov/sdmml.html Bayesian Nonparametrics Parametric vs Nonparametric

### Chapter 6. The stacking ensemble approach

82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

### INTRODUCING AZURE MACHINE LEARNING

David Chappell INTRODUCING AZURE MACHINE LEARNING A GUIDE FOR TECHNICAL PROFESSIONALS Sponsored by Microsoft Corporation Copyright 2015 Chappell & Associates Contents What is Machine Learning?... 3 The

### Data mining knowledge representation

Data mining knowledge representation 1 What Defines a Data Mining Task? Task relevant data: where and how to retrieve the data to be used for mining Background knowledge: Concept hierarchies Interestingness

### Data Mining Part 5. Prediction

Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

### Model Combination. 24 Novembre 2009

Model Combination 24 Novembre 2009 Datamining 1 2009-2010 Plan 1 Principles of model combination 2 Resampling methods Bagging Random Forests Boosting 3 Hybrid methods Stacking Generic algorithm for mulistrategy

### STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

### CLASSIFICATION AND CLUSTERING. Anveshi Charuvaka

CLASSIFICATION AND CLUSTERING Anveshi Charuvaka Learning from Data Classification Regression Clustering Anomaly Detection Contrast Set Mining Classification: Definition Given a collection of records (training

### Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví Pavel Kříž Seminář z aktuárských věd MFF 4. dubna 2014 Summary 1. Application areas of Insurance Analytics 2. Insurance Analytics

### Data Mining Prediction

Data Mining Prediction Jingpeng Li 1 of 23 What is Prediction? Predicting the identity of one thing based purely on the description of another related thing Not necessarily future events, just unknowns

### Analyzing Research Data Using Excel

Analyzing Research Data Using Excel Fraser Health Authority, 2012 The Fraser Health Authority ( FH ) authorizes the use, reproduction and/or modification of this publication for purposes other than commercial

### Statistical Machine Learning

Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes

### Role of Customer Response Models in Customer Solicitation Center s Direct Marketing Campaign

Role of Customer Response Models in Customer Solicitation Center s Direct Marketing Campaign Arun K Mandapaka, Amit Singh Kushwah, Dr.Goutam Chakraborty Oklahoma State University, OK, USA ABSTRACT Direct

### Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2. Tid Refund Marital Status

Data Mining Classification: Basic Concepts, Decision Trees, and Evaluation Lecture tes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Classification: Definition Given a collection of

### IBM SPSS Direct Marketing 23

IBM SPSS Direct Marketing 23 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 23, release

### Semester 1 Statistics Short courses

Semester 1 Statistics Short courses Course: STAA0001 Basic Statistics Blackboard Site: STAA0001 Dates: Sat. March 12 th and Sat. April 30 th (9 am 5 pm) Assumed Knowledge: None Course Description Statistical

### WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat

Information Builders enables agile information solutions with business intelligence (BI) and integration technologies. WebFOCUS the most widely utilized business intelligence platform connects to any enterprise

### Data Mining Practical Machine Learning Tools and Techniques

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. Hall Combining multiple models Bagging The basic idea

### Module 9: Nonparametric Tests. The Applied Research Center

Module 9: Nonparametric Tests The Applied Research Center Module 9 Overview } Nonparametric Tests } Parametric vs. Nonparametric Tests } Restrictions of Nonparametric Tests } One-Sample Chi-Square Test

### Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish

Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish Statistics Statistics are quantitative methods of describing, analysing, and drawing inferences (conclusions)

### Gerry Hobbs, Department of Statistics, West Virginia University

Decision Trees as a Predictive Modeling Method Gerry Hobbs, Department of Statistics, West Virginia University Abstract Predictive modeling has become an important area of interest in tasks such as credit

### Introduction to Statistics

1 Introduction to Statistics LEARNING OBJECTIVES After reading this chapter, you should be able to: 1. Distinguish between descriptive and inferential statistics. 2. Explain how samples and populations,

### Predicting Flight Delays

Predicting Flight Delays Dieterich Lawson jdlawson@stanford.edu William Castillo will.castillo@stanford.edu Introduction Every year approximately 20% of airline flights are delayed or cancelled, costing

### 1.1 What is Statistics?

1.1 What is Statistics? Statistics is the science that deals with the collection, analysis, and interpretation of numerical information. This science can be divided into two areas: descriptive statistics

Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm

### Statistics Review PSY379

Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses

### E-commerce Transaction Anomaly Classification

E-commerce Transaction Anomaly Classification Minyong Lee minyong@stanford.edu Seunghee Ham sham12@stanford.edu Qiyi Jiang qjiang@stanford.edu I. INTRODUCTION Due to the increasing popularity of e-commerce

### Lecture 2: Types of Variables

2typesofvariables.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lecture 2: Types of Variables Recap what we talked about last time Recall how we study social world using populations and samples. Recall

### COMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection.

COMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection. Instructor: (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/comp551 Unless otherwise

### Actuarial. Modeling Seminar Part 2. Matthew Morton FSA, MAAA Ben Williams

Actuarial Data Analytics / Predictive Modeling Seminar Part 2 Matthew Morton FSA, MAAA Ben Williams Agenda Introduction Overview of Seminar Traditional Experience Study Traditional vs. Predictive Modeling

### Machine Learning in Spam Filtering

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.

### Statistical Foundations: Measurement Scales. Psychology 790 Lecture #1 8/24/2006

Statistical Foundations: Measurement Scales Psychology 790 Lecture #1 8/24/2006 Today s Lecture Measurement What is always assumed. What we can say when we assign numbers to phenomena. Implications for

### Research Design Concepts. Independent and dependent variables Data types Sampling Validity and reliability

Research Design Concepts Independent and dependent variables Data types Sampling Validity and reliability Research Design Action plan for carrying out research How the research will be conducted to investigate

### SEIZE THE DATA. 2015 SEIZE THE DATA. 2015

1 Copyright 2015 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Deep dive into Haven Predictive Analytics Powered by HP Distributed R and

### Inferential Statistics

Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

### Car Insurance. Havránek, Pokorný, Tomášek

Car Insurance Havránek, Pokorný, Tomášek Outline Data overview Horizontal approach + Decision tree/forests Vertical (column) approach + Neural networks SVM Data overview Customers Viewed policies Bought

### Supervised Learning (Big Data Analytics)

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. Can be used

### TURKISH ORACLE USER GROUP

TURKISH ORACLE USER GROUP Data Mining in 30 Minutes Husnu Sensoy Global Maksimum Data & Information Tech. Founder VLDB Expert Agenda Who am I? Different problems of Data Mining In database data mining?!?

### Elementary Statistics

Elementary Statistics Chapter 1 Dr. Ghamsary Page 1 Elementary Statistics M. Ghamsary, Ph.D. Chap 01 1 Elementary Statistics Chapter 1 Dr. Ghamsary Page 2 Statistics: Statistics is the science of collecting,

### Impact of Boolean factorization as preprocessing methods for classification of Boolean data

Impact of Boolean factorization as preprocessing methods for classification of Boolean data Radim Belohlavek, Jan Outrata, Martin Trnecka Data Analysis and Modeling Lab (DAMOL) Dept. Computer Science,

### Calculating the Probability of Returning a Loan with Binary Probability Models

Calculating the Probability of Returning a Loan with Binary Probability Models Associate Professor PhD Julian VASILEV (e-mail: vasilev@ue-varna.bg) Varna University of Economics, Bulgaria ABSTRACT The

### Predictive Data modeling for health care: Comparative performance study of different prediction models

Predictive Data modeling for health care: Comparative performance study of different prediction models Shivanand Hiremath hiremat.nitie@gmail.com National Institute of Industrial Engineering (NITIE) Vihar

### Model Deployment. Dr. Saed Sayad. University of Toronto 2010 saed.sayad@utoronto.ca. http://chem-eng.utoronto.ca/~datamining/

Model Deployment Dr. Saed Sayad University of Toronto 2010 saed.sayad@utoronto.ca http://chem-eng.utoronto.ca/~datamining/ 1 Model Deployment Creation of the model is generally not the end of the project.

### Knowledge Discovery from patents using KMX Text Analytics

Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers

### Data Mining Practical Machine Learning Tools and Techniques. Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank Engineering the input and output Attribute selection Scheme independent, scheme

### Data Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.

Sept 03-23-05 22 2005 Data Mining for Model Creation Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.com page 1 Agenda Data Mining and Estimating Model Creation

### Predictive Modeling Techniques in Insurance

Predictive Modeling Techniques in Insurance Tuesday May 5, 2015 JF. Breton Application Engineer 2014 The MathWorks, Inc. 1 Opening Presenter: JF. Breton: 13 years of experience in predictive analytics

### Knowledge Discovery and Data Mining

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

### The Operational Value of Social Media Information. Social Media and Customer Interaction

The Operational Value of Social Media Information Dennis J. Zhang (Kellogg School of Management) Ruomeng Cui (Kelley School of Business) Santiago Gallino (Tuck School of Business) Antonio Moreno-Garcia

### Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions

SMA 50: Statistical Learning and Data Mining in Bioinformatics (also listed as 5.077: Statistical Learning and Data Mining ()) Spring Term (Feb May 200) Faculty: Professor Roy Welsch Wed 0 Feb 7:00-8:0

### IBM SPSS Direct Marketing 22

IBM SPSS Direct Marketing 22 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 22, release

### The Data Mining Process

Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data

### Predictive Modeling of Titanic Survivors: a Learning Competition

SAS Analytics Day Predictive Modeling of Titanic Survivors: a Learning Competition Linda Schumacher Problem Introduction On April 15, 1912, the RMS Titanic sank resulting in the loss of 1502 out of 2224