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
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.
Linear regression combining predictions from:
Gradient boosting machine
Support vector machine
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attack and whether you’re a criminal—as well as other ambitious predictions such as when you’ll die and whether your unpublished book will be a bestseller.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.
Figure from https://github.com/leekgroup/postpi/.
|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|
This work was financially supported by the Climate & Applied Forest Research Institute at SUNY-ESF.
Slides available at mm218.dev/esip2022