by Katrien Antonio, Jonas Crevecoeur and Roel Henckaerts
Course materials for the Hands-on Machine Learning in R course (September 2021).
📆 Module 1 on September 7 & 8, 2021, Module 2 on September 9 & 10, 2021
and Module 3 on September 13 & 14, 2021
🕣 From 9.00 am to 12.00 pm
or from 1.30 pm to 4.30 pm
📍 online, organized by Capgemini Academy
and Nationale Nederlanden
Course materials will be posted in the week before the workshop. You are now on the landing page for Module 2: tree-based machine learning methods.
The workshop requires a basic understanding of R. A good starting level is the material covered in the werkt-U-al-met-R? repo on Katrien’s GitHub.
Familiarity with statistical or machine learning methods is not required. The workshop gradually builds up these concepts, with an emphasis on hands-on demonstrations and exercises.
The R and RStudio software should be up and running before coming to the workshop. Please visit the Software requirements posted below.
This workshop introduces the essential concepts of building machine learning models with R. Throughout the workshop you will gain insights in the foundations of machine learning methods, including resampling methods, data preprocessing steps and the tuning of parameters. You will cover a variety of statistical and machine learning methods, ranging from GLMs, over tree-based machine learning methods to neural networks. You will acquire insights in the foundations of these methods, learn how to set-up the model building process, and focus on building a good understanding of the resulting model output and predictions.
Leaving this workshop, you should have a firm grasp of the working principles of a variety of machine learning methods and be able to explore their use in practical settings. Moreover, you should have acquired the fundamental insights to explore some other methods on your own.
The schedule will gradually be completed over the next days. The detailed schedule is subject to small changes.
| Module | Session | Duration | Description | Lecture material | R script | R solutions |
|---|---|---|---|---|---|---|
| Prework | your own pace | check the Prework and Software Requirements instructions | ||||
| Prework | day before the workshop | download the course material from the GitHub repo | ||||
| Module 2 | Session 1 | 9.00 - 9.20 | Basics of trees | sheets | ||
| 9.20 - 9.40 | Toy example for regression | sheets | script | |||
| 9.40 - 10.00 | Pruning via cross-validation | sheets | script | |||
| 10.10 - 10.30 | Toy example for classification | sheets | script | |||
| 10.30 - 11.00 | Claim frequency and severity modeling with {rpart} | sheets | script | |||
| 11.10 - 12.00 | Interpretation tools | sheets | script | |||
| Module 2 | Session 2 | 13.30 - 13.50 | Interpretation tools | sheets | script | |
| 13.50 - 14.30 | Bagging | sheets | script | |||
| 14.40 - 15.10 | Random forests | sheets | script and script | |||
| 15.10 - 15.30 | Gradient boosting | sheets | script | |||
| 15.40 - 16.00 | Tuning gbm’s | sheets | script | |||
| 16.00 - 16.15 | Claim frequency and severity with gbm’s | sheets | script | |||
| 16.15 - 16.30 | Using XGBoost | sheets | script |
In two sessions we cover:
- tree basics
- toy examples for regression and classification
- pruning via cross-validation
- claim frequency and severity modeling with tree-based methods
- interpretation tools
- bagging
- from bagging to random forests
- (stochastic) Gradient Boosting Machines
- tuning strategies
- claim frequency and severity modeling with {gbm}
- XGBoost
Download lecture sheets in pdf.
You have two options to join the coding exercises covered during the workshop. Either you join the RStudio cloud workspace dedicated to the workshop, and then you’ll run R in the cloud, from your browser. Or you use your local installation of R and RStudio.
We kindly ask participants to join the RStudio Cloud as default!
You will join our workspace on R Studio Cloud. This enables a very accessible set-up for working with R in the cloud for the less experienced user!
https://rstudio.cloud/spaces/109060/join?access_code=s3jVNlJn6J8SlZpSXmwvBsCMUC3XicxoFv5kCbCO
Here are the steps you should take (before the workshop):
- visit the above link
- log in by creating an account for RStudio Cloud or by using your Google or GitHub login credentials
- join the space
- at the top of your screen you see ‘Projects’, click ‘Projects’
- with the ‘copy’ button (on the right) you can make your own version of the ‘Module 2 - Tree-based machine learning methods’ project; in this copy you can work on the exercises, add comments etc.
- you should now be able to visit the project and see the ‘scripts’ and ‘data’ folders on the right. Open and run the ‘installation-instructions.R’ script from the scripts folder, to see if everything works fine.
We will have everything set up for you in the correct way. You only have to login!
Alternatively, you can bring a laptop with a recent version of R and RStudio installed. Make sure you can connect your laptop to the internet (or download the course material one day before the start of the workshop). You will need:
- R (at least 3.5.2 https://cloud.r-project.org/bin/windows/base/)
- RStudio (https://www.rstudio.com/products/rstudio/download/#download)
In the prework folder you will find a step-by-step guide to installing
R and RStudio (though a bit outdated).
Please run the below script in your R session to install the required packages (for Module 1, more packages will be added later).
Make sure to download the course material (including scripts and data sets) from the GitHub repo one day before the workshop. Use the green button Code > Download ZIP on the GitHub repo website. As a final step, after downloading, unpack the ZIP folder and make sure you can easily access the material (in particular scripts and data sets).
packages <- c("tidyverse", "here", "rstudioapi", "rpart", "rpart.plot", "vip", "pdp", "ipred",
"ranger", "gbm", "xgboost", "rstudioapi", "gganimate", "transformr",
"Ckmeans.1d.dp", "classInt", "gifski", "DiagrammeR")
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
if(sum(!(packages %in% installed.packages()[, "Package"]))) {
stop(paste('The following required packages are not installed:\n',
paste(packages[which(!(packages %in% installed.packages()[, "Package"]))], collapse = ', ')));
} else {
message("Everything is set up correctly. You are ready to go.")
}These instructions can also be downloaded as .R script from the
scripts folder. Open this script in RStudio (via File > Open file),
select the code and press the Run button top right in the script window.
Katrien Antonio is professor in insurance data science at KU Leuven and associate professor at University of Amsterdam. She teaches courses on data science for insurance, life and non-life insurance mathematics and loss models. Research-wise Katrien puts focus on pricing, reserving and fraud analytics, as well as mortality dynamics.
Jonas Crevecoeur is a post-doctoral researcher in biostatistics at KU Leuven. He recently obtained his PhD within the insurance research group at KU Leuven and holds the degrees of MSc in Mathematics, MSc in Insurance Studies and MSc in Financial and Actuarial Engineering (KU Leuven). Before starting the PhD program he worked as an intern with QBE Re (Belgium office) where he studied multiline products and copulas. Jonas was a PhD fellow of the Research Foundation - Flanders (FWO, PhD fellowship fundamental research).
Roel Henckaerts is a PhD student in insurance data science at KU Leuven. Roel holds the degrees of MSc in Mathematical Engineering, MSc in Insurance Studies and Financial and Actuarial Engineering (KU Leuven). Before starting the PhD program he worked as an intern with AIG (London office) and KBC. Roel is PhD fellow of the Research Foundation - Flanders (FWO, PhD fellowship strategic basic research). After the completion of his PhD, Roel will join Prophecy Labs: an AI/ML startup with experience in building end-to-end data solutions that provide concrete business value.
Happy learning!