Greg Cocks A Machine Learning-Based Approach For Flash Flood Susceptibility Mapping Considering Rainfall Extremes In The Northeast Region Of Bangladesh
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https://doi.org/10.1016/j.asr.2024.10.047 <-- shared paper
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#GIS #spatial #mapping #water #hydrology #climatechange #extremeweather #flood #flooding #flashflood #maps #susceptibility #Bangladesh #remotesensing #field #historical #rainfall #extremes #AI #machinelearning #ANN #RNN #RFGB #CatBoost #risk #hazard #model #modeling #planning #management #preparation #prediction #precipitation
#catboost
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Müssen es immer rechenintensive #deeplearning Modelle sein oder reichen für manche Anwendungsfälle auch leichtgewichtigere Alternativen, wie #xgboost, #catboost, #lightgbm oder gar klassische Methoden wie #SVMs und #logisticregression?
Auf der #M3 Konferenz in #Köln (vom 23.04.2025 bis zum 25.04.2024) werde ich diese Frage im Rahmen eines Vortrags diskutieren. Mehr Informationen unter: https://www.m3-konferenz.de/veranstaltung-21724-0-good-enough-for-biomedical-signals--wenn-klassische-ml-verfahren-besser-sind-als-neuronale-netze.html
Minds Mastering Machines 2024Minds Mastering Machines 2024
I'm excited to see #gradientboosting making some news! There is so much #aihype around #llms (and before that it was #deeplearning) but I think that for most #datascientists working in industry the development of #gradientboosting #machinelearning algorithms (like #xgboost and #catboost) are the real revolution and will have a much more long lived impact on our work.
NatureGradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data - Scientific ReportsWe sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than $$10^4$$ . Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes.
#MachineLearning lesson of the day: Working with a #gradientboosting model, I got no traction cross-validating hyperparameters like tree depth and number; but different evaluation metrics (e.g. SMSE vs. MAE etc.) had a major impact. Have you tried this?
IMHO #DNN get all the press because they do sexy human jobs like seeing and processing language. But in the business world of tabular data, #gradientboosting is where the real revolution is happening! #xgboost #catboost #lightgbm #DataScience