- Feb 2024
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docdrop.org docdrop.org
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Cette vidéo présente une formation sur le développement de l'empathie et des compétences émotionnelles pour améliorer le climat scolaire. Elle est animée par Bénédicte Art, inspectrice pédagogique régionale de l'académie de Créteil, qui partage son expertise et ses outils sur ce sujet.
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- Jan 2024
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You know XGBoost, but do you know NGBoost? I'd passed over this one, mentioned to me by someone wanting confidence intervals in their classification models. This could be an interesting paper to add to the ML curriculum.
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- Aug 2023
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www.nature.com www.nature.com
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forecasts of the magnitude and longevity of the event were very good; although forecasted high-temperature records fell 1–3 °C short of the observed highs in many cases.
- for: meteorology, forecasting, ensemble forecasting, Pacific Northwest heatwave
- paraphrase
- forecasts of the magnitude and longevity of the event were very good;
- although forecasted high-temperature records fell 1–3 °C short of the observed highs in many cases.
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Meteorologists are typically reluctant to make extreme weather forecasts at forecast horizons of around a week for fear of “crying wolf” and the associated reduction in end-user trust. In this case, however, the ensemble forecast provided sufficient certainty that meteorologists were able to warn of “extreme” heat at this relatively long-lead time—a testament to ensemble forecast technology.
- for meteorology, forecasting, ensemble forecasting
- paraphrase
- Meteorologists are typically reluctant to make extreme weather forecasts at forecast horizons of around a week
- for fear of “crying wolf” and
- the associated reduction in end-user trust.
- In this case, however, the ensemble forecast provided sufficient certainty that meteorologists were able to warn of “extreme” heat at this relatively long-lead time
- a testament to ensemble forecast technology.
- Meteorologists are typically reluctant to make extreme weather forecasts at forecast horizons of around a week
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- May 2023
- Dec 2020
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www.cs.bham.ac.uk www.cs.bham.ac.uk
Tags
Annotators
URL
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- Aug 2020
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www.medrxiv.org www.medrxiv.org
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Ray, E. L., Wattanachit, N., Niemi, J., Kanji, A. H., House, K., Cramer, E. Y., Bracher, J., Zheng, A., Yamana, T. K., Xiong, X., Woody, S., Wang, Y., Wang, L., Walraven, R. L., Tomar, V., Sherratt, K., Sheldon, D., Reiner, R. C., Prakash, B. A., … Consortium, C.-19 F. H. (2020). Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S. MedRxiv, 2020.08.19.20177493. https://doi.org/10.1101/2020.08.19.20177493
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- Jul 2019
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machinelearningmastery.com machinelearningmastery.com
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Ensemble Machine Learning Algorithms in Python with scikit-learn
Read on July 4, 2019
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- Nov 2016
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journals.plos.org journals.plos.org
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Whilst the consensus method we used provided the best predictions under AUC assessment – seemingly confirming its potential for reducing model-based uncertainty in SDM predictions [58], [59] – its accuracy to predict changes in occupancy was lower than most single models. As a result, we advocate great care when selecting the ensemble of models from which to derive consensus predictions; as previously discussed by Araújo et al. [21], models should be chosen based on aspects of their individual performance pertinent to the research question being addressed, and not on the assumption that more models are better.
It's interesting that the ensembles perform best overall but more poorly for predicting changes in occupancy. It seems possible that ensembling multiple methods is basically resulting in a more static prediction, i.e., something closer to a naive baseline.
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