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💧🌏 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> &lt;-- 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>
💧🌏 Greg Cocks<p>Avalanche Debris Detection From Sentinel-2 Data Using Fuzzy Machine Learning And Colour Spaces For The Indian Himalaya<br>--<br><a href="https://doi.org/10.1080/2150704X.2025.2488532" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1080/2150704X.2025.</span><span class="invisible">2488532</span></a> &lt;-- 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/snowavalanches" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>snowavalanches</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/avalanches" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>avalanches</span></a> <a href="https://techhub.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://techhub.social/tags/fuzzyclassification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>fuzzyclassification</span></a> <a href="https://techhub.social/tags/SVM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SVM</span></a> <a href="https://techhub.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</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/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/forecasting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>forecasting</span></a> <a href="https://techhub.social/tags/risk" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>risk</span></a> <a href="https://techhub.social/tags/hazard" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>hazard</span></a> <a href="https://techhub.social/tags/massmovement" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>massmovement</span></a> <a href="https://techhub.social/tags/engineeringgeology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>engineeringgeology</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/imagery" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>imagery</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/change" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>change</span></a> <a href="https://techhub.social/tags/debris" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>debris</span></a> <a href="https://techhub.social/tags/detection" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>detection</span></a> <a href="https://techhub.social/tags/satellite" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>satellite</span></a> <a href="https://techhub.social/tags/sentinel" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sentinel</span></a> <a href="https://techhub.social/tags/Himalaya" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Himalaya</span></a> <a href="https://techhub.social/tags/Himalayas" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Himalayas</span></a> <a href="https://techhub.social/tags/performance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>performance</span></a></p>
Fiona Gregory<p>This series of videos on machine learning algorithms (Lab a through Lab d, so far) by Courage Kamusoko are the best explanations I've seen yet. How the models actually work, their strengths and weaknesses, what you are actually solving for when you tune the hyperparameters, and examples in Python. <a href="https://www.youtube.com/@couragekamusoko5689/videos" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">youtube.com/@couragekamusoko56</span><span class="invisible">89/videos</span></a><br><a href="https://mapstodon.space/tags/SVM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SVM</span></a> <a href="https://mapstodon.space/tags/KNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>KNN</span></a> <a href="https://mapstodon.space/tags/DecisionTree" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionTree</span></a> <a href="https://mapstodon.space/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a></p>
arildsen@fosstodon.org moved<p>If I were running a blog on applying <a href="https://fosstodon.org/tags/randomforest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomforest</span></a> models to various problems, I would call it The Statistical Lumberjack 🤔</p>
SIG UCA<p><strong>Presentación tesis Teledetección-Machine Learning 02-2024 - 2025_02_07 13_45 CST - Recording</strong></p> <p><a href="https://makertube.net/videos/watch/289c7549-8f6e-40f3-9045-9b97c7aba5b7" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">makertube.net/videos/watch/289</span><span class="invisible">c7549-8f6e-40f3-9045-9b97c7aba5b7</span></a></p>
iCode2<p>I scaled up the popular Palmer Penguins machine learning dataset from 344 rows to 100k rows using adversarial random forest, with an accuracy of 88%.</p><p>Now, you have more rows of data with which to train your classification models.</p><p>You can download it here, along with R &amp; Python scripts, to load and view the dataset: <a href="https://ieee-dataport.org/documents/palmer-penguins-100k-0" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">ieee-dataport.org/documents/pa</span><span class="invisible">lmer-penguins-100k-0</span></a></p><p>Have a dataset you want to scale up? Say hello!</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/randomforest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomforest</span></a> <a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <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/datasets" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datasets</span></a> <a href="https://mastodon.social/tags/syntheticdatageneration" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>syntheticdatageneration</span></a> <a href="https://mastodon.social/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ai</span></a></p>
Antoine Brias<p>I’ve tackled a comparative study of Random Forest vs. XGBoost on ecological data from Yaquina Bay. This post dives into Model performance and<br>Feature importance.</p><p>I’d love to hear your thoughts and feedback! Read it here:<br><a href="https://www.briaslab.fr/blog/?action=view&amp;url=oiuambeh" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">briaslab.fr/blog/?action=view&amp;</span><span class="invisible">url=oiuambeh</span></a><br><a href="https://fediscience.org/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://fediscience.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> <a href="https://fediscience.org/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a> <a href="https://fediscience.org/tags/XGBoost" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>XGBoost</span></a> <a href="https://fediscience.org/tags/FeatureImportance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FeatureImportance</span></a></p>
Kyle Taylor<p>CV and <a href="https://hostux.social/tags/RemoteSensing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RemoteSensing</span></a> folks are starting to discover <a href="https://hostux.social/tags/boosting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>boosting</span></a>.</p><p>...Models were developed using individual and combined datasets. Sentinel-2 optical data outperformed Sentinel-1 radar data, but the fusion of both sensors achieved the highest accuracy (R2 &gt; 0.7, RMSE = 105.64 Mg/ha, MAE = 85.34 Mg/ha) .... machine learning models significantly improved AGB estimates compared to traditional regression techniques, and gradient boosters outperformed <a href="https://hostux.social/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a>. ..</p><p>[1] <a href="https://www.sciencedirect.com/science/article/abs/pii/S2352938524001551" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">sciencedirect.com/science/arti</span><span class="invisible">cle/abs/pii/S2352938524001551</span></a></p>
Thibaut Vidal<p>Essentially, each path in a <a href="https://mas.to/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a> to a leaf indicates that a number of training examples satisfy a sequence of constraints (from the splits). Inferring training data boils down to finding a set of examples satisfying all these constraints, a bit like placing numbers on a Sudoku...</p>
Jesus Castagnetto 🇵🇪<p>"Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers"</p><p><a href="https://arxiv.org/abs/2402.01502" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2402.01502</span><span class="invisible"></span></a></p><p>'... Despite their remarkable effectiveness and broad application, the drivers of success underlying ensembles of trees are still not fully understood. In this paper, we highlight how interpreting tree ensembles as adaptive and self-regularizing smoothers can provide new intuition and deeper insight to this topic...'</p><p><a href="https://mastodon.social/tags/MachineaLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineaLearning</span></a> <a href="https://mastodon.social/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://mastodon.social/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a></p>
Abdelkrim Bouasria<p>Proud to be part of this work where we (Yassine Bouslihim et al.) explored the effect of high resolution covariates in mapping the variability of soil organic matter and pH in northern Morocco.</p><p>Many thanks to Yassine Bouslihim and co-authors</p><p>Give it a try and best read!<br><a href="https://www.tandfonline.com/doi/full/10.1080/19475683.2024.2309868?src=exp-la" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">tandfonline.com/doi/full/10.10</span><span class="invisible">80/19475683.2024.2309868?src=exp-la</span></a></p><p><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/soilscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>soilscience</span></a> <a href="https://fosstodon.org/tags/soilcarbon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>soilcarbon</span></a> <a href="https://fosstodon.org/tags/soilfertility" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>soilfertility</span></a> <a href="https://fosstodon.org/tags/digital_soil_mapping" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>digital_soil_mapping</span></a> <a href="https://fosstodon.org/tags/agriculture" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>agriculture</span></a> <a href="https://fosstodon.org/tags/morocco" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>morocco</span></a> <a href="https://fosstodon.org/tags/randomforest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomforest</span></a></p>
NIOO-KNAW<p>New publication: Well known indicator groups do not predict the decline of insects. <a href="https://social.edu.nl/tags/insects" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>insects</span></a> <a href="https://social.edu.nl/tags/speciesdecline" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>speciesdecline</span></a> <a href="https://social.edu.nl/tags/randomforest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomforest</span></a> <a href="https://social.edu.nl/tags/biodiversity" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>biodiversity</span></a> <a href="https://social.edu.nl/tags/globalchange" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>globalchange</span></a> <br><a href="https://doi.org/10.1016/j.ecolind.2023.111458" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1016/j.ecolind.2023</span><span class="invisible">.111458</span></a></p>
NIOO-KNAW<p>New NIOO publication: Threats to the <a href="https://social.edu.nl/tags/soilmicrobiome" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>soilmicrobiome</span></a> from <a href="https://social.edu.nl/tags/nanomaterials" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nanomaterials</span></a>: A global meta and machine-learning analysis.<br><a href="https://social.edu.nl/tags/biodiversity" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>biodiversity</span></a> <a href="https://social.edu.nl/tags/metaanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>metaanalysis</span></a> <a href="https://social.edu.nl/tags/randomforest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomforest</span></a> <br><a href="https://doi.org/10.1016/j.soilbio.2023.109248" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1016/j.soilbio.2023</span><span class="invisible">.109248</span></a></p>
Dirk Van den Poel<p>My online lecture of the <a href="https://mastodon.online/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BigData</span></a> class is on using <a href="https://mastodon.online/tags/PySpark" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PySpark</span></a> for machine learning using Spark <a href="https://mastodon.online/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NLP</span></a> (natural language processing) for <a href="https://mastodon.online/tags/classification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>classification</span></a> and <a href="https://mastodon.online/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a>. <a href="https://mastodon.online/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.online/tags/orms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>orms</span></a> <a href="https://mastodon.online/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://mastodon.online/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> <a href="https://mastodon.online/tags/dataanalytics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataanalytics</span></a> <a href="https://mastodon.online/tags/jupyter" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>jupyter</span></a> <a href="https://mastodon.online/tags/notebook" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>notebook</span></a> <a href="https://mastodon.online/tags/randomforest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomforest</span></a> <a href="https://mastodon.online/tags/word2vec" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>word2vec</span></a> <a href="https://mastodon.online/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a></p>
Jennifer Lin<p>A follow-up on the decision tree series leading to a random forest this time with details on model building, imbalanced dataset, feature importances &amp; hyperparameter tuning - <a href="https://jhylin.github.io/Data_in_life_blog/posts/17_ML2-2_Random_forest/1_random_forest.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">jhylin.github.io/Data_in_life_</span><span class="invisible">blog/posts/17_ML2-2_Random_forest/1_random_forest.html</span></a></p><p>Jupyter notebook link: <a href="https://github.com/jhylin/ML2-2_random_forest/blob/main/1_random_forest.ipynb" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/jhylin/ML2-2_random</span><span class="invisible">_forest/blob/main/1_random_forest.ipynb</span></a></p><p>Post updated to show a different max_features used for regression task (thanks <span class="h-card" translate="no"><a href="https://sciencemastodon.com/@dr_greg_landrum" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>dr_greg_landrum</span></a></span> for pointing this out)</p><p><a href="https://fosstodon.org/tags/ml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ml</span></a> <a href="https://fosstodon.org/tags/randomforest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomforest</span></a> <a href="https://fosstodon.org/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>scikitlearn</span></a> <a href="https://fosstodon.org/tags/pandas" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pandas</span></a> <a href="https://fosstodon.org/tags/seaborn" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>seaborn</span></a> <a href="https://fosstodon.org/tags/matplotlib" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>matplotlib</span></a> <a href="https://fosstodon.org/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://fosstodon.org/tags/cheminformatics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cheminformatics</span></a> <a href="https://fosstodon.org/tags/chembl" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>chembl</span></a> <a href="https://fosstodon.org/tags/drugdiscovery" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>drugdiscovery</span></a></p>
Teresita Porter 🙋🏻‍♀️<p>Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems</p><p><a href="https://onlinelibrary.wiley.com/doi/full/10.1111/mec.17149" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">onlinelibrary.wiley.com/doi/fu</span><span class="invisible">ll/10.1111/mec.17149</span></a></p><p><a href="https://ecoevo.social/tags/DroughtStress" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DroughtStress</span></a> <a href="https://ecoevo.social/tags/WaterStatus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>WaterStatus</span></a> <a href="https://ecoevo.social/tags/fungi" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>fungi</span></a> <a href="https://ecoevo.social/tags/bacteria" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bacteria</span></a> <a href="https://ecoevo.social/tags/biomarkers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>biomarkers</span></a> <a href="https://ecoevo.social/tags/forest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>forest</span></a> <a href="https://ecoevo.social/tags/eDNA" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>eDNA</span></a> <a href="https://ecoevo.social/tags/metabarcoding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>metabarcoding</span></a> <a href="https://ecoevo.social/tags/ASVs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ASVs</span></a> <a href="https://ecoevo.social/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a></p>
Joxean Koret (@matalaz)<p>One question for the <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> people: what approach do you use to determine if a decision trees or a random forest approach should work better? Do you simply try both approaches and use whatever seems to work better?</p><p>According to what I read, decision trees are more prone to overfitting, while random forest is a more complex approach. Which means little to me 😅 </p><p><a href="https://mastodon.social/tags/ml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ml</span></a> <a href="https://mastodon.social/tags/DecisionTrees" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionTrees</span></a> <a href="https://mastodon.social/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a> <a href="https://mastodon.social/tags/Overfitting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Overfitting</span></a></p>
Fabrizio Musacchio<p>Here is an example of using <a href="https://sigmoid.social/tags/RandomForests" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForests</span></a> 🌳🌳 for <a href="https://sigmoid.social/tags/PixelClassification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PixelClassification</span></a> 🖼️ in <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> 🐍, using <span class="h-card"><a href="https://fosstodon.org/@napari" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>napari</span></a></span> for labeling ✍️</p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-06-23-_random_forests_pixel_classifier/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-06-23-_random_forests_pixel_classifier/</span></a></p><p><a href="https://sigmoid.social/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a> <a href="https://sigmoid.social/tags/Napari" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Napari</span></a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://sigmoid.social/tags/ImageProcessing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ImageProcessing</span></a> <a href="https://sigmoid.social/tags/Bioimage" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bioimage</span></a> <a href="https://sigmoid.social/tags/BioimageAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BioimageAnalysis</span></a></p>
Fabrizio Musacchio<p>Ever wondered how <a href="https://sigmoid.social/tags/DecisionTrees" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionTrees</span></a> and <a href="https://sigmoid.social/tags/RandomForests" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForests</span></a> 🌳🌳 are related? Here is a quick <a href="https://sigmoid.social/tags/tutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tutorial</span></a> that compares both methods in terms of <a href="https://sigmoid.social/tags/classification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>classification</span></a> and <a href="https://sigmoid.social/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> ✌️</p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-06-22-_decision_trees_vs_random_forests/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-06-22-_decision_trees_vs_random_forests/</span></a></p><p><a href="https://sigmoid.social/tags/DecisionTree" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionTree</span></a> <a href="https://sigmoid.social/tags/RandomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RandomForest</span></a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a></p>
Corey Bradshaw (Kaurna Land)<p>How to use <a href="https://mastodon.world/tags/randomForest" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>randomForest</span></a> for predicting community interactions</p><p><a href="https://onlinelibrary.wiley.com/doi/10.1111/ecog.06619" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">onlinelibrary.wiley.com/doi/10</span><span class="invisible">.1111/ecog.06619</span></a></p>