R has a wide variety of machine learning (ML) models. While the many ML functions solve similar problems by predicting various outcomes, they use a confusing array of different command styles, making them hard to learn. Fortunately, the caret package provides a standard approach to dozens of ML functions, greatly speeding learning and use. This two-day hands-on workshop starts with ML basics and takes you step-by-step through increasingly complex modeling styles.
Photography by Steve Chastain http://www.stevechastainphotography.com/
Most of our time will be spent working through examples that you may run simultaneously on your computer. You will see both the instructor’s screen and yours, side-by-side, as we run the examples and discuss the output. However, the handouts include each step for people who prefer to just take notes.
This workshop is available at your organization’s site, or via webinars.
The 0n-site version is the most engaging by far, generating much discussion and occasionally veering off briefly to cover topics specific to a particular organization. The instructor presents a topic for around twenty minutes. Then we switch to exercises, which are already open in another tabbed window. The exercises contain hints that show the general structure of the solution; you adapt those hints to get the final solution. The complete solutions are in a third tabbed window, so if you get stuck the answers are a click away. The typical schedule for training on site is located here.
A webinar version is also available. The approach saves travel expenses and is especially useful for organizations with branch offices. It’s offered as two half-day sessions, often with a day or two skipped in between to give participants a chance to do the exercises on their own and catch up on other work. There is time for questions on the lecture topics (live) and the exercises (via email). However, webinar participants are typically much less engaged, and far less discussion takes place.
For further details or to arrange a webinar or site visit, contact the instructor, Bob Muenchen, at firstname.lastname@example.org.
This workshop assumes a basic knowledge of R. Introductory knowledge of statistics is helpful, but not required.
When finished, participants will be able to use R to apply the most popular and effective machine learning models to make predictions and assess the likely accuracy of those predictions.
Bob is an ASA Accredited Professional Statistician™ with 35 years of experience and is currently the manager of OIT Research Computing Support (formerly the Statistical Consulting Center) at the University of Tennessee. He has taught workshops on research computing topics for more than 500 organizations and has presented workshops in partnership with the American Statistical Association, RStudio, DataCamp.com,New Horizons Computer Learning Centers, Revolution Analytics (acquired by Microsoft), and Xerox Learning Services. Bob has written or coauthored over 70 articles published in scientific journals and conference proceedings and has provided guidance on more than 1,000 graduate theses and dissertations.
Bob has served on the advisory boards of SAS Institute, SPSS Inc., StatAce OOD, the Statistical Graphics Corporation, and PC Week Magazine. His suggested improvements have been incorporated into SAS, SPSS, StatAce, JMP, jamovi, BlueSky Statistics, STATGRAPHICS and numerous R packages. His research interests include statistical computing, data graphics and visualization, text analytics, and data mining.
On-site training is best done in a computer lab with a projector and, for large rooms, a PA system. The webinar version is delivered to your computer using Zoom (or similar webinar systems if your organization has a preference.)
Course programs, data, and exercises will be sent to you a week before the workshop. The instructions include installing R, which you can download R for free here: http://www.r-project.org/. We will also use RStudio, which you can download for free here: http://RStudio.com. If you already know a different R editor, that’s fine too.
1.2 Preparing Your Computer
1.3 Note to System Administrators
1.4 Workshop Files
OVERVIEW OF MACHINE LEARNING
2.2 Goals of Machine Learning
2.3 Types of Problems
2.4 Types of Machine Learning
2.5 Statistical Models
2.6 Heuristic Models
2.7 Bagging Models
2.8 Boosting Models
2.9 Data Set Size
2.10 Model Building Steps
2.11 Allocating Data: Train, Validation, & Test
2.12 Types of Splits
2.13 Fitting Issues
2.14 Feature Selection
2.15 Feature Selection Approaches
INTRO TO THE caret PACKAGE
3.1 Formula Method vs. Non-formula
3.2 Each R Package May Predict Differently
3.3 Goals of the caret Package
3.4 caret References
3.5 Types of Models in caret
3.6 Type of Predictions by Model
3.7 Types of Functions in caret
4.1 The caret::preProcess Function
4.2 preProcess Tasks
4.3 Pre-processing the titanic Dataset
4.4 Examining Factor Variables
4.5 Setting Value to Model
4.6 Examining Numeric Variables
4.6 Examining Relation to Target
4.7 Creating Some Problems to Diagnose
4.8 Counting Missing Values
4.9 Visualizing Missing Values
4.10 Imputing Missing Values
4.11 Finding High Correlations
4.12 Finding High Correlation Automatically
4.13 Getting Names of Highly Correlated vars
4.14 Finding Variables with Near Zero Variance (NZV)