Tree-Based Models
Learn decision trees, random forests, and boosted models in R. This one-day live online course covers CART, bagging, boosting, and model interpretation for applied data analysis.
Duration: 1 Days, 6 hours per day
Next Date: December 2, 2025
Format: Live Online Format
UK (GMT) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£150Registration Fee
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- Overview
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Course Description
This 1-day course provides an in-depth introduction to tree-based models in R. Decision trees and their ensemble extensions (random forests, bagging, and boosting) are among the most powerful and interpretable machine learning methods available. They can capture nonlinear relationships and complex interactions without requiring strict distributional assumptions, making them ideal for ecological, epidemiological, and applied data science problems. This course builds from the foundations of regression and classification trees to ensemble methods, with a strong emphasis on interpretability, model tuning, and practical implementation in R.
What You’ll Learn
During the course will cover the following:
- Understand the logic and structure of decision trees for regression and classification.
- Learn how tree models partition data and handle nonlinear relationships and interactions.
- Implement and interpret CART (Classification and Regression Tree) models using R.
- Understand overfitting, pruning, and model complexity control.
- Fit and interpret ensemble tree methods, including bagging and random forests.
- Apply boosted trees using xgboost or lightgbm for high-performance prediction.
- Evaluate model accuracy using cross-validation and out-of-bag (OOB) error.
Interpret variable importance and partial dependence plots for explainability
Course Format
Interactive Learning Format
Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
Target audience: ecologists, environmental scientists, public-health analysts, data scientists, postgraduate students, and early-career researchers.
Assumed computer background: Basic experience using R and RStudio (e.g., importing data, running simple functions)
Assumed quantitative background: Familiarity with descriptive statistics and linear regression concepts.
Required statistical experience: Familiarity with linear models is helpful but not essential – concepts will be reviewed
Not required but helpful: Experience with data wrangling (e.g., using dplyr or tidyr), basic plotting with ggplot2, and reading model output
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Niamh Mimnagh
Niamh is a statistician working at the interface of ecology, epidemiology, and data science. Her research focuses on applying and developing statistical and machine learning methods to address real-world challenges such as estimating species population sizes from count and trace data and predicting livestock disease re-emergence using sparse or imbalanced datasets. She works with a wide array of statistical approaches, including Bayesian hierarchical models, N-mixture models, anomaly detection algorithms, and spatial analysis techniques.
Niamh earned her PhD in Statistics, with a focus on multispecies abundance modelling, and holds a first-class MSc in Data Science. Alongside her research, she is actively engaged in science communication and education, running a popular blog on applied statistics for non-specialists, and regularly delivering workshops and guest lectures on topics such as GLMs and machine learning with imbalanced data.
Education & Career
- PhD in Statistics (Multispecies Abundance Modelling)
- MSc in Data Science (First Class Honours)
- Instructor, consultant, and science communicator in statistical ecology and epidemiology
Research Focus
Niamh’s work centres on extracting meaningful insights from complex ecological and epidemiological data. She is particularly interested in population estimation techniques and predictive modelling for conservation and disease management, using advanced statistical tools and reproducible workflows.
Current Projects
- Development of Bayesian and ML approaches for estimating species abundance from imperfect data
- Modelling livestock disease risk using spatial and temporal predictors
- Creating accessible educational materials for teaching applied statistics in R
Professional Consultancy
Niamh provides expert statistical support to academic and applied research projects, with a focus on ecological monitoring, conservation planning, and disease modelling. She also advises on study design and data workflows for interdisciplinary teams.
Teaching & Skills
- Teaches topics including GLMs, Bayesian statistics, machine learning for imbalanced data, and spatial statistics in R
- Advocates for reproducibility, open science, and accessible statistical training
- Experienced in communicating complex methods to broad audiences
Links
Session 1 – 01:20:00 – Introduction to Decision Trees
We begin with the theory behind decision trees for regression and classification. Topics include recursive partitioning, splitting criteria (Gini impurity, deviance, variance reduction), and visualising tree structures. Participants will build and interpret simple trees using the rpart and rpart.plot packages, exploring how trees model nonlinear relationships and interactions naturally.
Session 2 – 01:20:00 – Controlling Complexity and Avoiding Overfitting
This session covers model tuning, pruning, and cross-validation. Participants will learn how to interpret complexity parameters (cp) and select the optimal tree size. We also discuss bias–variance trade-offs and demonstrate how pruning can improve generalisation.
Session 3 – 01:20:00 – Ensemble Methods: Bagging and Random Forests
We introduce ensemble learning as a solution to instability in single trees. Participants learn the concept of bootstrap aggregation (bagging) and how random forests improve predictive accuracy by reducing variance. The session includes implementation in R with randomForest and ranger, interpretation of variable importance metrics, and assessing out-of-bag error.
Session 4 – 01:20:00 – Boosted Trees and Model Interpretation
The final session focuses on boosting algorithms such as xgboost and lightgbm. We explain how boosting sequentially builds strong models from weak learners, and demonstrate parameter tuning, feature importance plots, and SHAP or partial dependence plots for interpretation. The session concludes with a comparative case study evaluating tree-based methods on a real dataset.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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