Next in our #MachineLearning series: We build our very own decision tree (a Random Forest!) using #Ruby and Scikit-learn 🐍

This is a very popular tree-based ensemble model. Check it out 👉 https://www.ombulabs.ai/blog/introduction-to-random-forests  

#AI #ML #RandomForest #Python

Geomorphic Factors Impact Groundwater Levels More Than Harvesting in a Coast Redwood Forest
--
https://doi.org/10.1002/hyp.70302 <-- shared paper
--
#water #hydrology #groundwater #stream #baseflow #plant #gegetation #transpiration #growingseason #forest #management #watershed #mediterraneanclimate #California #redwood #harvesting #soils #geomorphology #aquiferdepth #slope #randomforest #model #modeling #coast #coastal #rainfill #precipitation #infiltration
#USFS

🌦️ main predictors of presence were meteorological factors (temp, atmo pressure, rainfall)

🌱 low (<3m) vegetation drives abundances,

🌳 more mosquitoes in urban parks and residential (with gardens) areas than in densely built areas.

#rstats #randomforest #openscience

🌳 Random Forests and Living Trees

English translation of my earlier article on applying satellite imagery and machine learning to map urban land cover.

What started as a local research project in Kryvyi Rih turned into something much larger — the results sparked a heated discussion among residents, officials, and industry representatives about the real condition of green buffers around large industrial sites.

The methodology developed during that work is still being used today — adapted for new environmental and urban projects.

🔗 https://www.datastory.org.ua/random-forests-and-living-trees/

#RemoteSensing #MachineLearning #LandCoverMapping #UrbanEcology #EnvironmentalMonitoring #RandomForest #GeospatialAnalysis #GIS #RStats #SAGAGIS #QGIS #IndependentResearch #OpenSource #EnvironmentalDataScience #KryvyiRih #LULC

Next in our #MachineLearning series: We build our very own decision tree (a Random Forest!) using #Ruby and Scikit-learn 🐍

This is a very popular tree-based ensemble model. Check it out 👉 https://go.fastruby.io/l1y 

#AI #ML #RandomForest #Python

Assessment Of Snow Cover Dynamics And The Effects Of Environmental Drivers In High Mountain Ecosystems
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https://doi.org/10.1016/j.eiar.2025.107969 <-- shared paper
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#GIS #spatial #mapping #remotesensing #earthobservation #snow #ice #snowcover #dynamics #climatechange #mountains #ecosystems #spatialanalysis #spatiotemporal #MODIS #model #modeling #extremeweather #water #hydrology #climate #zones #trendanalysis #linearregression #RandomForest #cryosphere

Avalanche Debris Detection From Sentinel-2 Data Using Fuzzy Machine Learning And Colour Spaces For The Indian Himalaya
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared paper
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#GIS #spatial #mapping #snowavalanches #snow #avalanches #machinelearning #fuzzyclassification #SVM #AI #randomforest #model #modeling #forecasting #risk #hazard #massmovement #engineeringgeology #remotesensing #earthobservation #imagery #spatialanalysis #spatiotemporal #change #debris #detection #satellite #sentinel #Himalaya #Himalayas #performance

Exploring Model Templates Across Disciplines 📊

At the @tuberlin workshop “Large Language Models for HPSS” Maximilian Noichl presented #OpenAlexMapper—a tool to trace how model templates, concepts, and methods like #RandomForest spread across scientific disciplines over time.

His talk offered a compelling case for combining projective methods with large-scale bibliometric data.

https://maxnoichl.eu/full/talks/talk_BERLIN_April_2025/talkBERLIN.html#/section-3

#LLMs #MachineLearning #HistoryOfScience #DigitalHumanities #Mapping

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. https://www.youtube.com/@couragekamusoko5689/videos
#SVM #KNN #DecisionTree #RandomForest

If I were running a blog on applying #randomforest models to various problems, I would call it The Statistical Lumberjack 🤔

Presentación tesis Teledetección-Machine Learning 02-2024 - 2025_02_07 13_45 CST - Recording

https://makertube.net/videos/watch/289c7549-8f6e-40f3-9045-9b97c7aba5b7

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%.

Now, you have more rows of data with which to train your classification models.

You can download it here, along with R & Python scripts, to load and view the dataset: https://ieee-dataport.org/documents/palmer-penguins-100k-0

Have a dataset you want to scale up? Say hello!

#machinelearning #randomforest #rstats #python #datascience #datasets #syntheticdatageneration #ai

I’ve tackled a comparative study of Random Forest vs. XGBoost on ecological data from Yaquina Bay. This post dives into Model performance and
Feature importance.

I’d love to hear your thoughts and feedback! Read it here:
https://www.briaslab.fr/blog/?action=view&url=oiuambeh
#MachineLearning #DataScience #RandomForest #XGBoost #FeatureImportance

CV and #RemoteSensing folks are starting to discover #boosting.

...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 > 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 #RandomForest. ..

[1] https://www.sciencedirect.com/science/article/abs/pii/S2352938524001551

Machine Learning – Regression Cheat Sheet | How To Perform Regression

Learn about machine learning regression algorithms, tools, & tips #xgboost #randomforest #decisiontree #svm #glm #gbm. source

https://quadexcel.com/wp/machine-learning-regression-cheat-sheet-how-to-perform-regression/

Essentially, each path in a #RandomForest 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...

"Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers"

https://arxiv.org/abs/2402.01502

'... 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...'

#MachineaLearning #ML #RandomForest

Next in our #MachineLearning series: We build our very own decision tree (a Random Forest!) using #Ruby and Scikit-learn 🐍

This is a very popular tree-based ensemble model. Check it out 👉 https://go.fastruby.io/jsh

#AI #ML #RandomForest #Python

Next in our #MachineLearning series: We build our very own decision tree (a Random Forest!) using #Ruby and Scikit-learn 🐍

This is a very popular tree-based ensemble model. Check it out 👉 https://go.fastruby.io/jsh

#AI #ML #RandomForest #Python

【R コードの説明】ランダムフォレストを用いた腸内マイクロバイオームデータからの予測モデルの構築と評価
https://qiita.com/diegokawashima/items/0663d2224cb8ab0dffe7?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #R #randomForest #モデル構築 #マイクロバイオーム