Machine Learning Basics: Decision Trees
Learn how to move beyond simple sorting to uncover the hidden patterns in your donor data.
Traditional donor modeling uses one-size-fits-all equations that simply sort constituents into lists—but modern fundraising demands more.
Your organization has data that could reveal powerful insights about donor behavior and giving patterns, but linear modeling only tells you who gave, not why. You’re missing the hidden segments and nuanced patterns that could transform your engagement strategy.
Without machine learning skills, you’re stuck with basic sorting when you need sophisticated segmentation that drives real results.
Machine Learning Basics: Decision Trees teaches you how to move beyond simple sorting to uncover the hidden patterns in your donor data.
Instructor Marianne Pelletier guides you through practical machine learning techniques that reveal distinct audience segments for more effective engagement.
What you’ll learn:
- Core concepts of machine learning and how it differs from traditional linear modeling
- Practical experience with multiple decision tree methods (RPART, JRIP, Random Forest)
- Testing methods and presenting findings effectively to non-technical audiences
Prerequisites: Experience in building linear models, including data preparation, significance testing, and modeling; and ability to use R for data science.
Through hands-on examples and exercises, you’ll transform your fundraising analytics from basic lists to sophisticated segmentation that identifies which donors to engage, when, and how.
Your Instructor
Marianne Pelletier has over 35 years of fundraising experience, with the majority in prospect research and prospecting. She is one of the first adopters of donor modeling and data mining techniques. Her professional experience includes prospect research, both as a research analyst for Harvard and Lesley Universities and as a department director for Carnegie Mellon University and Cornell University.
Pelletier’s multi-decade career also includes running an annual giving program with average increased revenues of 27% per year; and providing software consulting through the Datatel Corporation, teaching clients both how to use their new software and assisting them with better analysis and more efficient processing. She led the analytics division of the Helen Brown Group for over 10 years, and remains an associate.
A recipient of a lifetime achievement award from the New England Development Research Association and a Woman of the Year designation by the National Association of Professional Women, Pelletier has served as a volunteer for both election campaigns and social service agencies. She has served on the boards of the New England Development Research Association, the Upstate New York chapter of APRA, and ending as secretary for the APRA International board.
A prolific writer, she has published articles on management, prospect research, fundraising, and personal growth on a range of platforms. A sought-after speaker, she often speaks on how to start an analytics shop, along with a variety of other subjects related to prospecting.
Course Curriculum
Introduction
- What is Machine Learning
- Machine Learning vs. Linear Modeling
- Getting and Installing WEKA
Decision Tree Methods
- First Method: RPART
- Second Method: JRIP
- Third Method: Random Forest in R
- Fourth Method: Random Forest in WEKA
Wrap Up: Testing and Presenting Findings
- Testing Methods: RPART and Random Forest from R
- Testing Methods: JRIP, and Random Forest from WEKA
- Presenting Findings to the Lay Audience
Advanced Tips
- Advanced Tips
- Going Beyond this Course
Course Wrap Up
- Wrapping Things Up
- Resources
Master decision tree algorithms to unlock sophisticated donor segmentation and predictive modeling in your fundraising analytics.
This hands-on course takes you beyond traditional linear modeling into the world of machine learning, where you’ll discover how decision trees can reveal complex patterns in donor behavior that simple sorting methods miss. Working with practical tools like WEKA and R, you’ll learn multiple decision tree methods—including RPART, JRIP, and Random Forest—and gain the skills to test, validate, and present your findings to stakeholders who need actionable insights, not technical jargon.
$597.00
Frequently Asked Questions
When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course – you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like – across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.