💧🌏 Greg Cocks<p>Assessment Of Snow Cover Dynamics And The Effects Of Environmental Drivers In High Mountain Ecosystems<br>--<br><a href="https://doi.org/10.1016/j.eiar.2025.107969" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1016/j.eiar.2025.10</span><span class="invisible">7969</span></a> <-- shared paper<br>--<br><a href="https://techhub.social/tags/GIS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GIS</span></a> <a href="https://techhub.social/tags/spatial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spatial</span></a> <a href="https://techhub.social/tags/mapping" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mapping</span></a> <a href="https://techhub.social/tags/remotesensing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>remotesensing</span></a> <a href="https://techhub.social/tags/earthobservation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>earthobservation</span></a> <a href="https://techhub.social/tags/snow" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>snow</span></a> <a href="https://techhub.social/tags/ice" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ice</span></a> <a href="https://techhub.social/tags/snowcover" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>snowcover</span></a> <a href="https://techhub.social/tags/dynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dynamics</span></a> <a href="https://techhub.social/tags/climatechange" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>climatechange</span></a> <a href="https://techhub.social/tags/mountains" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mountains</span></a> <a href="https://techhub.social/tags/ecosystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ecosystems</span></a> <a href="https://techhub.social/tags/spatialanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spatialanalysis</span></a> <a href="https://techhub.social/tags/spatiotemporal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spatiotemporal</span></a> <a href="https://techhub.social/tags/MODIS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MODIS</span></a> <a href="https://techhub.social/tags/model" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>model</span></a> <a href="https://techhub.social/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://techhub.social/tags/extremeweather" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>extremeweather</span></a> <a href="https://techhub.social/tags/water" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>water</span></a> <a href="https://techhub.social/tags/hydrology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>hydrology</span></a> <a href="https://techhub.social/tags/climate" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>climate</span></a> <a href="https://techhub.social/tags/zones" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>zones</span></a> <a href="https://techhub.social/tags/trendanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>trendanalysis</span></a> <a href="https://techhub.social/tags/linearregression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearregression</span></a> <a href="https://techhub.social/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a> <a href="https://techhub.social/tags/cryosphere" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cryosphere</span></a></p>
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#linearregression
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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>
Eric Maugendre<p><a href="https://social.coop/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://social.coop/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a> vs <a href="https://social.coop/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>explainability</span></a> 🧵</p><p>"The explanations themselves can be difficult to convey to nonexperts, such as end users and line-of-business teams" <a href="https://www.techtarget.com/searchenterpriseai/feature/Interpretability-vs-explainability-in-AI-and-machine-learning" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">techtarget.com/searchenterpris</span><span class="invisible">eai/feature/Interpretability-vs-explainability-in-AI-and-machine-learning</span></a></p><p><a href="https://social.coop/tags/AIEthics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIEthics</span></a> <a href="https://social.coop/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compliance</span></a> <a href="https://social.coop/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>taxonomy</span></a> <a href="https://social.coop/tags/ethicalAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ethicalAI</span></a> <a href="https://social.coop/tags/AIEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIEvaluation</span></a> <a href="https://social.coop/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://social.coop/tags/trust" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>trust</span></a> <a href="https://social.coop/tags/neuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuralNetworks</span></a> <a href="https://social.coop/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://social.coop/tags/governance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>governance</span></a> <a href="https://social.coop/tags/AIgovernance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIgovernance</span></a> <a href="https://social.coop/tags/safety" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>safety</span></a> <a href="https://social.coop/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</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>Redressing <a href="https://hachyderm.io/tags/Bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bias</span></a>: "Correlation Constraints for Regression Models":<br>Treder et al (2021) <a href="https://doi.org/10.3389/fpsyt.2021.615754" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.3389/fpsyt.2021.615</span><span class="invisible">754</span></a></p><p><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/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/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</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/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/skLearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>skLearn</span></a> <a href="https://hachyderm.io/tags/scikitLearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>scikitLearn</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/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a></p>
Eric Maugendre<p>"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."<br>Longford (2005) <a href="http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://www.</span><span class="ellipsis">stat.columbia.edu/~gelman/stuf</span><span class="invisible">f_for_blog/longford.pdf</span></a></p><p><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/nullHypothesis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nullHypothesis</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/pValues" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pValues</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/statisticalLiteracy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statisticalLiteracy</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/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inference</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/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a></p>
Eric Maugendre<p>Feature Selection in Python; a script ready to use: <a href="https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">johfischer.com/2021/08/06/corr</span><span class="invisible">elation-based-feature-selection-in-python-from-scratch/</span></a></p><p><a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/featureSelection" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>featureSelection</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/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/bigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bigData</span></a> <a href="https://hachyderm.io/tags/classification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>classification</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/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/Schusterbauer" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Schusterbauer</span></a> <a href="https://hachyderm.io/tags/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inference</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a></p>
Eric Maugendre<p>"Feature importance helps in understanding which features contribute most to the prediction"</p><p>A few lines with <a href="https://hachyderm.io/tags/sklearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sklearn</span></a>: <a href="https://mljourney.com/sklearn-linear-regression-feature-importance/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">mljourney.com/sklearn-linear-r</span><span class="invisible">egression-feature-importance/</span></a> </p><p><a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>explainability</span></a> <a href="https://hachyderm.io/tags/AIethics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIethics</span></a> <a href="https://hachyderm.io/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compliance</span></a> <a href="https://hachyderm.io/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>taxonomy</span></a> <a href="https://hachyderm.io/tags/ethicalAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ethicalAI</span></a> <a href="https://hachyderm.io/tags/AIevaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIevaluation</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/featureEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>featureEngineering</span></a></p>
Eric Maugendre<p><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>"The following sections discuss several state-of-the-art interpretable and explainable <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> methods. The selection of works does not comprise an exhaustive survey of the literature. Instead, it is meant to illustrate the commonest properties and inductive biases behind interpretable models and [black-box] explanation methods using concrete instances."<br><a href="https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493#widm1493-sec-0010-title" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">wires.onlinelibrary.wiley.com/</span><span class="invisible">doi/full/10.1002/widm.1493#widm1493-sec-0010-title</span></a> 🧵</p><p><a href="https://hachyderm.io/tags/interpretability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>interpretability</span></a> <a href="https://hachyderm.io/tags/explainability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>explainability</span></a> <a href="https://hachyderm.io/tags/aiethics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>aiethics</span></a> <a href="https://hachyderm.io/tags/compliance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compliance</span></a> <a href="https://hachyderm.io/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>taxonomy</span></a> <a href="https://hachyderm.io/tags/ethicalai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ethicalai</span></a> <a href="https://hachyderm.io/tags/aievaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>aievaluation</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a></p>
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