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848 results for “pyOpenSci”
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Propensity for Simulacra examines a world where systems perfect behaviour by erasing humanity itself.
It extends the ideas in When the Camera Becomes the Conscience.
#Simulacra #Philosophy #Baudrillard #SpeculativeFiction #CriticalTheory #RidleyPark #PostmodernThought #Blog #AmReading #AmWriting #Books #Dystopia
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Propensity for Simulacra examines a world where systems perfect behaviour by erasing humanity itself.
It extends the ideas in When the Camera Becomes the Conscience.
#Simulacra #Philosophy #Baudrillard #SpeculativeFiction #CriticalTheory #RidleyPark #PostmodernThought #Blog #AmReading #AmWriting #Books #Dystopia
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Propensity for Simulacra examines a world where systems perfect behaviour by erasing humanity itself.
It extends the ideas in When the Camera Becomes the Conscience.
#Simulacra #Philosophy #Baudrillard #SpeculativeFiction #CriticalTheory #RidleyPark #PostmodernThought #Blog #AmReading #AmWriting #Books #Dystopia
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Propensity Score Matching (PSM): как обойтись без A/B-теста и всё равно узнать правду
Как определить, влияет ли то или иное событие на ключевые метрики, если полноценный A/B-тест недоступен? В этой статье мы разберём метод Propensity Score Matching (PSM ): узнаем, как компенсировать отсутствие рандомизации, выровнять группы по ключевым признакам и избежать ложных выводов при оценке эффекта.
https://habr.com/ru/articles/887276/
#psm #abtest #mashinelearning #mashine_learning #propensity_score_matching #statistics #машинное_обучение #абтесты #статистика #product
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Propensity Score Matching (PSM): как обойтись без A/B-теста и всё равно узнать правду
Как определить, влияет ли то или иное событие на ключевые метрики, если полноценный A/B-тест недоступен? В этой статье мы разберём метод Propensity Score Matching (PSM ): узнаем, как компенсировать отсутствие рандомизации, выровнять группы по ключевым признакам и избежать ложных выводов при оценке эффекта.
https://habr.com/ru/articles/887276/
#psm #abtest #mashinelearning #mashine_learning #propensity_score_matching #statistics #машинное_обучение #абтесты #статистика #product
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Propensity Score Matching (PSM): как обойтись без A/B-теста и всё равно узнать правду
Как определить, влияет ли то или иное событие на ключевые метрики, если полноценный A/B-тест недоступен? В этой статье мы разберём метод Propensity Score Matching (PSM ): узнаем, как компенсировать отсутствие рандомизации, выровнять группы по ключевым признакам и избежать ложных выводов при оценке эффекта.
https://habr.com/ru/articles/887276/
#psm #abtest #mashinelearning #mashine_learning #propensity_score_matching #statistics #машинное_обучение #абтесты #статистика #product
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Propensity Score Matching (PSM): как обойтись без A/B-теста и всё равно узнать правду
Как определить, влияет ли то или иное событие на ключевые метрики, если полноценный A/B-тест недоступен? В этой статье мы разберём метод Propensity Score Matching (PSM ): узнаем, как компенсировать отсутствие рандомизации, выровнять группы по ключевым признакам и избежать ложных выводов при оценке эффекта.
https://habr.com/ru/articles/887276/
#psm #abtest #mashinelearning #mashine_learning #propensity_score_matching #statistics #машинное_обучение #абтесты #статистика #product
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‘You’re Such a #Moron!’: #ElonMusk #Destroyed on #SocialMedia for #Posting #Fake #Article That #Compares #DonaldTrump to #AdolfHitler, Violating #X’s #Community #Rules.
Protector of #freespeech, or #purveyor of #disinformation? #ElonMusk likes to cast himself as the former, but as his #fervor for #DonaldTrump has grown, so has his #propensity for spreading #disinformation.
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The propensity of a large number of magick practitioners/publishers/teachers to conspiracy theories, bad bio-medical takes and ill politics really puts me off the broader art - a lot. I still use it in my own way, but we’re going to miss the rigour of the likes of Peter J. Carroll #magick #conspiracytheories #PeterJCarroll #occult
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CW: Gangbang, Cuck, Caption
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CW: Gangbang, Cuck, Caption
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CW: Gangbang, Cuck, Caption
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CW: Gangbang, Cuck, Caption
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The propensity of academics to fight #strawmen is too damn high.
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The propensity of academics to fight #strawmen is too damn high.
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The propensity of academics to fight #strawmen is too damn high.
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Gemini’s propensity for self-loathing
Saving these here so I can include them in future slide decks:
#gemini #machineSociology #modelPsychology #modelWelfare -
Gemini’s propensity for self-loathing
Saving these here so I can include them in future slide decks:
#gemini #machineSociology #modelPsychology #modelWelfare -
Gemini’s propensity for self-loathing
Saving these here so I can include them in future slide decks:
#gemini #machineSociology #modelPsychology #modelWelfare -
Gemini’s propensity for self-loathing
Saving these here so I can include them in future slide decks:
#gemini #machineSociology #modelPsychology #modelWelfare -
A new #study using #PropensityBench, a benchmark for measuring #AIagents’ propensity to use #harmfultools, found that #realisticpressures like #deadlines and #financiallosses significantly increase #misbehaviour rates. The study tested a dozen models from various companies across nearly 6,000 scenarios, revealing that even under zero pressure, the average failure rate was 19%. https://spectrum.ieee.org/ai-agents-safety?eicker.news #tech #media #news
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A new #study using #PropensityBench, a benchmark for measuring #AIagents’ propensity to use #harmfultools, found that #realisticpressures like #deadlines and #financiallosses significantly increase #misbehaviour rates. The study tested a dozen models from various companies across nearly 6,000 scenarios, revealing that even under zero pressure, the average failure rate was 19%. https://spectrum.ieee.org/ai-agents-safety?eicker.news #tech #media #news
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A new #study using #PropensityBench, a benchmark for measuring #AIagents’ propensity to use #harmfultools, found that #realisticpressures like #deadlines and #financiallosses significantly increase #misbehaviour rates. The study tested a dozen models from various companies across nearly 6,000 scenarios, revealing that even under zero pressure, the average failure rate was 19%. https://spectrum.ieee.org/ai-agents-safety?eicker.news #tech #media #news
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A new #study using #PropensityBench, a benchmark for measuring #AIagents’ propensity to use #harmfultools, found that #realisticpressures like #deadlines and #financiallosses significantly increase #misbehaviour rates. The study tested a dozen models from various companies across nearly 6,000 scenarios, revealing that even under zero pressure, the average failure rate was 19%. https://spectrum.ieee.org/ai-agents-safety?eicker.news #tech #media #news
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A new #study using #PropensityBench, a benchmark for measuring #AIagents’ propensity to use #harmfultools, found that #realisticpressures like #deadlines and #financiallosses significantly increase #misbehaviour rates. The study tested a dozen models from various companies across nearly 6,000 scenarios, revealing that even under zero pressure, the average failure rate was 19%. https://spectrum.ieee.org/ai-agents-safety?eicker.news #tech #media #news
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Julie introduces Inverse Propensity-of-Censoring Weighting (IPCW), which consists in re-weighting non-censored individuals with a weight that is inversely proportional to the probability of being censored.
This leads her to introduce two classical metrics of evaluation of the quality of the prediction of survival: the Briar score (equivalent to the MSE) and the C-index (equivalent to the ROC-AUC score).
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Julie introduces Inverse Propensity-of-Censoring Weighting (IPCW), which consists in re-weighting non-censored individuals with a weight that is inversely proportional to the probability of being censored.
This leads her to introduce two classical metrics of evaluation of the quality of the prediction of survival: the Briar score (equivalent to the MSE) and the C-index (equivalent to the ROC-AUC score).
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Julie introduces Inverse Propensity-of-Censoring Weighting (IPCW), which consists in re-weighting non-censored individuals with a weight that is inversely proportional to the probability of being censored.
This leads her to introduce two classical metrics of evaluation of the quality of the prediction of survival: the Briar score (equivalent to the MSE) and the C-index (equivalent to the ROC-AUC score).
-
Julie introduces Inverse Propensity-of-Censoring Weighting (IPCW), which consists in re-weighting non-censored individuals with a weight that is inversely proportional to the probability of being censored.
This leads her to introduce two classical metrics of evaluation of the quality of the prediction of survival: the Briar score (equivalent to the MSE) and the C-index (equivalent to the ROC-AUC score).
-
Julie introduces Inverse Propensity-of-Censoring Weighting (IPCW), which consists in re-weighting non-censored individuals with a weight that is inversely proportional to the probability of being censored.
This leads her to introduce two classical metrics of evaluation of the quality of the prediction of survival: the Briar score (equivalent to the MSE) and the C-index (equivalent to the ROC-AUC score).