About Me

  • Mike Mahoney

  • PhD Student at SUNY-ESF and ESIP Community Fellow

  • Focusing on visualization, VR and ML as ways to think about large-scale systems

  • These slides: mm218.dev/esip2022

New York State 2019 CLCPA:

  • 85% reduction in GHG emissions by 2050

  • Net zero emissions statewide by 2050

  • Lands & forests make up the difference

  • Need a way to monitor sequestration

Figure from Shirer, R. and Zimmerman, C. (2010) Forest Regeneration in New York State. The Nature Conservancy.


Modeling approach:

  • Ensemble modeling approach

  • Linear regression combining predictions from:

    • Random forest

    • Gradient boosting machine

    • Support vector machine

  • Ensembling improves predictive accuracy, lets us try many models but present a single prediction


The press will have you believe that machine learning can reliably predict whether you’re gay, whether you’ll develop psychosis, whether you’ll have a heart attack and whether you’re a criminal—as well as other ambitious predictions such as when you’ll die and whether your unpublished book will be a bestseller. It’s all a lie.

Here’s how the lie works. Researchers report high “accuracy,” but then later reveal—buried within the details of a technical paper—that they were actually misusing the word “accuracy” to mean another measure of performance related to accuracy but in actuality not nearly as impressive.

This is the mainstream opinion of AI and machine learning.

How can we build back lost trust when developing AI systems that might impact people’s lives?


Only report the accuracy that matters

Figure from https://github.com/leekgroup/postpi/.

Report accuracy spatially

Report accuracy across scales

Plot-pixel comparison 39.60 1.88 0.76
8,660 ha hex 33.27 3.56 0.76
78,100 ha hex 23.38 1.64 0.80
216,500 ha hex 21.17 0.75 0.81

Report applicability domains

Remaining Challenges

  • How can we tailor our assessments to be the most useful for end users?

  • How can we avoid overwhelming people with charts and tables when they just want a map?

Thank you!

This work was financially supported by the Climate & Applied Forest Research Institute at SUNY-ESF.

Find me online:




Slides available at mm218.dev/esip2022