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#modelevaluation

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Journal of Plant Ecology<p>💻 <a href="https://mastodon.social/tags/AllometricEquations" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AllometricEquations</span></a> for estimating above- and below-ground <a href="https://mastodon.social/tags/Biomass" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Biomass</span></a> of <a href="https://mastodon.social/tags/PhragmitesAustralisMarshes" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PhragmitesAustralisMarshes</span></a>.<br>Characteristics:<br>1️⃣ Divided into saltwater marshes and freshwater marshes.<br>2️⃣ Using plant height as the sole predictor.<br>3️⃣ It is a power-law allometric model.<br><a href="https://mastodon.social/tags/ModelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelEvaluation</span></a><br><a href="https://doi.org/10.1093/jpe/rtae113" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.1093/jpe/rtae113</span><span class="invisible"></span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.<br>Regression Redress restrains bias by segregating the residual values.<br>My article: <a href="http://data.yt/kit/regression-redress.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">data.yt/kit/regression-redress</span><span class="invisible">.html</span></a></p><p><a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>distributions</span></a> <a href="https://hachyderm.io/tags/accuracy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>accuracy</span></a> <a href="https://hachyderm.io/tags/RegressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RegressionRedress</span></a> <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://hachyderm.io/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>How to assess a statistical model?<br>How to choose between variables?</p><p>Pearson's <a href="https://hachyderm.io/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> is irrelevant if you suspect that the relationship is not a straight line.</p><p>If monotonic relationship:<br>"<a href="https://hachyderm.io/tags/Spearman" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Spearman</span></a>’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".<br>"<a href="https://hachyderm.io/tags/Kendall" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Kendall</span></a>’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."<br>Ref: <a href="https://statisticseasily.com/kendall-tau-b-vs-spearman/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticseasily.com/kendall-t</span><span class="invisible">au-b-vs-spearman/</span></a></p><p><a href="https://hachyderm.io/tags/normality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>normality</span></a> <a href="https://hachyderm.io/tags/normalDistribution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>normalDistribution</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>featureEngineering</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/Pearson" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pearson</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/regressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regressionRedress</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>distributions</span></a></p>
LavX News<p>Why Your Evaluation Process is the Key to AI Success</p><p>In the rapidly evolving landscape of large language models (LLMs), the choice of model is critical, but even more crucial is the evaluation process you employ. This article explores how a custom eval ...</p><p><a href="https://news.lavx.hu/article/why-your-evaluation-process-is-the-key-to-ai-success" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.lavx.hu/article/why-your-</span><span class="invisible">evaluation-process-is-the-key-to-ai-success</span></a></p><p><a href="https://mastodon.social/tags/news" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>news</span></a> <a href="https://mastodon.social/tags/tech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tech</span></a> <a href="https://mastodon.social/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLM</span></a> <a href="https://mastodon.social/tags/AIdevelopment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIdevelopment</span></a> <a href="https://mastodon.social/tags/ModelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelEvaluation</span></a></p>
IB Teguh TM<p>Dive into the world of model evaluation in machine learning! Discover how to optimize logistic regression, compare ensemble techniques, and select the best model using key performance metrics. <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.social/tags/ModelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelEvaluation</span></a> <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a></p><p><a href="https://teguhteja.id/model-evaluation-mastering-performance-metrics-and-selection-in-machine-learning/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">teguhteja.id/model-evaluation-</span><span class="invisible">mastering-performance-metrics-and-selection-in-machine-learning/</span></a></p>
American Naturalist<p>There's still time to submit a proposal to “Genomic forecasting of adaptation under environmental change” special feature in AmNat! Click here for all the details: <a href="https://www.amnat.org/announcements/genomic-forecasting-feature.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">amnat.org/announcements/genomi</span><span class="invisible">c-forecasting-feature.html</span></a></p><p><a href="https://ecoevo.social/tags/genomicForecasting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>genomicForecasting</span></a> <a href="https://ecoevo.social/tags/Genomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Genomics</span></a> <a href="https://ecoevo.social/tags/theory" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>theory</span></a> <a href="https://ecoevo.social/tags/methods" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>methods</span></a> <a href="https://ecoevo.social/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a> <a href="https://ecoevo.social/tags/empiricalStudies" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>empiricalStudies</span></a> <a href="https://ecoevo.social/tags/criticalViewpoints" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>criticalViewpoints</span></a></p>
Data Blogger<p>Learn how to interpret R², the 'coefficient of determination' in regression models! 📈📚 <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> <a href="https://mastodon.social/tags/RegressionAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RegressionAnalysis</span></a> <a href="https://mastodon.social/tags/ModelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelEvaluation</span></a> <a href="https://towardsdatascience.com/interpreting-r%C2%B2-a-narrative-guide-for-the-perplexed-086a9a69c1ec" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/interpr</span><span class="invisible">eting-r%C2%B2-a-narrative-guide-for-the-perplexed-086a9a69c1ec</span></a></p>
Judith van Stegeren<p><a href="https://eugeneyan.com//writing/aieng-reflections/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">eugeneyan.com//writing/aieng-r</span><span class="invisible">eflections/</span></a></p><p>Very nice summary of the AI Engineer Summit 2023 by <span class="h-card" translate="no"><a href="https://recsys.social/@eugeneyan" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>eugeneyan</span></a></span> </p><p><a href="https://fosstodon.org/tags/llms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>llms</span></a> <a href="https://fosstodon.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://fosstodon.org/tags/mlops" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mlops</span></a> <a href="https://fosstodon.org/tags/chatgpt" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>chatgpt</span></a> <a href="https://fosstodon.org/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://fosstodon.org/tags/modelevaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelevaluation</span></a></p>
Daniele de Rigo<p>4/</p><p>Below, key points:</p><p>- "lack of <a href="https://hostux.social/tags/ModelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelEvaluation</span></a>"</p><p>- statistical <a href="https://hostux.social/tags/uncertainty" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>uncertainty</span></a> &amp; "<a href="https://hostux.social/tags/robustness" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>robustness</span></a> of event attribution results"</p><p><a href="https://hostux.social/tags/References" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>References</span></a></p><p>[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. <a href="https://purl.org/INRMM-MiD/z-ED8RQFV5" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">purl.org/INRMM-MiD/z-ED8RQFV5</span><span class="invisible"></span></a></p><p><a href="https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter11.pdf#page=28" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">ipcc.ch/report/ar6/wg1/downloa</span><span class="invisible">ds/report/IPCC_AR6_WGI_Chapter11.pdf#page=28</span></a></p>
Crypto News<p>9 Common interview questions for AI jobs - AI job seekers should be prepared to answer common interview ques... - <a href="https://cointelegraph.com/news/9-common-interview-questions-for-ai-jobs" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cointelegraph.com/news/9-commo</span><span class="invisible">n-interview-questions-for-ai-jobs</span></a> <a href="https://schleuss.online/tags/unsupervisedlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>unsupervisedlearning</span></a> <a href="https://schleuss.online/tags/interviewquestions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interviewquestions</span></a> <a href="https://schleuss.online/tags/supervisedlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>supervisedlearning</span></a> <a href="https://schleuss.online/tags/datapreprocessing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datapreprocessing</span></a> <a href="https://schleuss.online/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://schleuss.online/tags/modelevaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelevaluation</span></a> <a href="https://schleuss.online/tags/non" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>non</span></a>-technical <a href="https://schleuss.online/tags/collaboration" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>collaboration</span></a> <a href="https://schleuss.online/tags/technical" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>technical</span></a> <a href="https://schleuss.online/tags/aijobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>aijobs</span></a></p>
Scott Robeson<p>New open-access article on "Decomposition of the mean absolute error (MAE) into systematic and unsystematic components" in <a href="https://fediscience.org/tags/PLOSONE" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PLOSONE</span></a>, if you're into that sort of thing. <br><a href="https://fediscience.org/tags/Statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Statistics</span></a> <a href="https://fediscience.org/tags/ModelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelEvaluation</span></a> <a href="https://fediscience.org/tags/ModelError" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelError</span></a><br><a href="https://dx.plos.org/10.1371/journal.pone.0279774" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">dx.plos.org/10.1371/journal.po</span><span class="invisible">ne.0279774</span></a></p>
Tiago F. R. Ribeiro<p>Model Evaluation, Model Selection, and Algorithm<br>Selection in Machine Learning</p><p><a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.social/tags/ModelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelEvaluation</span></a> <a href="https://mastodon.social/tags/CrossValidation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CrossValidation</span></a> <br><a href="https://mastodon.social/tags/HyperparameterOptimization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HyperparameterOptimization</span></a> </p><p><a href="https://arxiv.org/pdf/1811.12808.pdf" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/pdf/1811.12808.pdf</span><span class="invisible"></span></a></p>
Roban Hultman Kramer<p>Anyway, I keep meaning to write up a blog post on “falsehoods I have believed about measuring model performance” touching on <a href="https://sigmoid.social/tags/AppliedML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AppliedML</span></a> issues related to <a href="https://sigmoid.social/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a>, <a href="https://sigmoid.social/tags/metrics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>metrics</span></a>, <a href="https://sigmoid.social/tags/monitoring" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>monitoring</span></a>, <a href="https://sigmoid.social/tags/observability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>observability</span></a>, and <a href="https://sigmoid.social/tags/experiments" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>experiments</span></a> (<a href="https://sigmoid.social/tags/RCTs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RCTs</span></a>). The cool kids would call this <a href="https://sigmoid.social/tags/AIAlignment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIAlignment</span></a> in their VC pitch decks, but even us <a href="https://sigmoid.social/tags/NormCore" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NormCore</span></a> ML engineers have to wrestle with how to measure and optimize the real-world impact of our models.</p>
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