4/

Below, key points:

- "lack of #ModelEvaluation"

- statistical #uncertainty & "#robustness of event attribution results"

#References

[4] Seneviratne, et al., 2021. Chapter 11: weather and climate extreme events in a changing climate. In: Climate Change 2021: The Physical Science Basis - Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, pp. 1513–1766. https://purl.org/INRMM-MiD/z-ED8RQFV5

https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter11.pdf#page=28

4/

Below, key points:

- "lack of #ModelEvaluation"

- statistical #uncertainty & "#robustness of event attribution results"

#References

[4] Seneviratne, et al., 2021. Chapter 11: weather and climate extreme events in a changing climate. In: Climate Change 2021: The Physical Science Basis - Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, pp. 1513–1766. https://purl.org/INRMM-MiD/z-ED8RQFV5

https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter11.pdf#page=28

9 Common interview questions for AI jobs - AI job seekers should be prepared to answer common interview ques... - https://cointelegraph.com/news/9-common-interview-questions-for-ai-jobs #unsupervisedlearning #interviewquestions #supervisedlearning #datapreprocessing #machinelearning #modelevaluation #non-technical #collaboration #technical #aijobs

Model Evaluation, Model Selection, and Algorithm
Selection in Machine Learning

#MachineLearning #ModelEvaluation #CrossValidation
#HyperparameterOptimization

https://arxiv.org/pdf/1811.12808.pdf

Anyway, I keep meaning to write up a blog post on “falsehoods I have believed about measuring model performance” touching on #AppliedML issues related to #modelEvaluation, #metrics, #monitoring, #observability, and #experiments (#RCTs). The cool kids would call this #AIAlignment in their VC pitch decks, but even us #NormCore ML engineers have to wrestle with how to measure and optimize the real-world impact of our models.