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1,114 datasets for “learning models”

  1. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 12 Feb 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  2. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 5 Mar 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  3. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 16 Mar 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  4. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 29 Mar 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  5. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 4 Apr 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  6. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 10 Apr 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  7. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 16 Apr 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  8. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 5 May 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  9. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 5 Jun 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  10. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 16 Jun 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  11. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 23 Jun 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  12. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 27 Jun 2026

    … 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model …

    • HTML
  13. data.gov.au

    Surficial and deep earth material prediction from geochemical compositions - a spatial predictive model

    Geoscience Australia DataUpdated 18 Nov 2025

    … The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive …

    • HTML
  14. data.gouv.fr

    BenchGecko - toutes les données autour de l' IA

    BenchGeckoUpdated 22 Apr 2026

    Jeu de données ouvert sur les modèles d'intelligence artificielle, leurs scores de benchmarks, leur tarification multi-fournisseurs …

    • CSV
  15. data.gov.au

    Revised weathering intensity model of Australia

    Geoscience Australia DataUpdated 16 Oct 2025

    … Here, I use a random forest decision tree machine learning algorithm to first establish a relationship between field …

    • PDF
  16. data.gov.au

    Training dataset for object detection - Penguins from UAV

    Australian Ocean Data NetworkUpdated 23 Jun 2025

    … The dataset consists of three folders: "train", containing 531 images, intended for model training; "valid", containing 50 images …

    • HTML
    • PNG
  17. data.gov.au

    Training dataset for object detection - Penguins from UAV

    Australian Ocean Data NetworkUpdated 17 Nov 2025

    … The dataset consists of three folders: "train", containing 531 images, intended for model training; "valid", containing 50 images …

    • HTML
    • PNG
  18. Data.gov Catalog

    Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers

    National Renewable Energy Laboratory (NREL)Updated 2 Jan 2024

    … This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first …

    • HTML
  19. Data.gov Catalog

    Airfoil Computational Fluid Dynamics - 9k shapes, 2 AoA's

    National Renewable Energy Laboratory (NREL)Updated 2 Jan 2024

    … This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first …

    • HTML
  20. data.gov.au

    Bool and Hacks Lagoons Landcover Model Output 2022

    Friends of Bool and Hacks LagoonsUpdated 20 Dec 2025

    … Machine Learning is notoriously data-hungry and model accuracy is sensitive to the quality and quantity of input …

    • SHP
“learning models” datasets · Open Data Finder