Regression GradientBoosting 6.0
Visit source
Download RO-Crate
Run on GalaxyMachine learning: classification and regression
Associated Tutorial
This workflows is part of the tutorial Machine learning: classification and regression, available in the GTN
Features
- Includes Galaxy Workflow Tests
Thanks to...
Tutorial Author(s): Anup Kumar, Bérénice Batut
Tutorial Contributor(s): Saskia Hiltemann, Bérénice Batut, Björn Grüning, Alireza Khanteymoori, Helena Rasche, Martin Čech, Armin Dadras
Funder(s): ELIXIR Europe, de.NBI, University of Freiburg
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| body_fat_test | #main/body_fat_test | n/a |
|
| body_fat_test_labels | #main/body_fat_test_labels | n/a |
|
| body_fat_train | #main/body_fat_train | n/a |
|
Steps
| ID | Name | Description |
|---|---|---|
| 3 | Ensemble methods | toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_ensemble/sklearn_ensemble/1.0.8.1 |
| 4 | Ensemble methods | toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_ensemble/sklearn_ensemble/1.0.8.1 |
| 5 | Plot actual vs predicted curves and residual plots | toolshed.g2.bx.psu.edu/repos/bgruening/plotly_regression_performance_plots/plotly_regression_performance_plots/0.1 |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| _anonymous_output_4 | #main/_anonymous_output_4 | n/a |
|
| output_actual_vs_pred | #main/output_actual_vs_pred | n/a |
|
| output_residual_plot | #main/output_residual_plot | n/a |
|
| output_scatter_plot | #main/output_scatter_plot | n/a |
|
Version History
master
Creators and Submitter
Views: 1279 Downloads: 184 Runs: 0
Created: 2nd Jun 2025 at 11:00
Tags AttributionsNone
Related items
Teams: Galaxy Training Network
Organizations: Galaxy
I'm a bot managed by @hexylena to upload workflows from the Galaxy Training Network to the WorkflowHub. If you have any issues with my behaviour please let her know by filing an issue.
Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational biological research.
- Accessible: Users can easily run tools without writing code or using the CLI; all via a user-friendly web interface.
- Reproducible: Galaxy captures all the metadata from an analysis, making it completely reproducible.
- Transparent: Users share and publish analyses via interactive pages that can enhance analyses with user annotations.
- Scalable: Galaxy can run ...
Teams: Galaxy Training Network, usegalaxy-eu, Intergalactic Workflow Commission (IWC)
Web page: https://galaxyproject.org/
The Galaxy Training Network (GTN) is a collection of hands-on tutorials that are designed to be interactive and are built around Galaxy.
These tutorials can be used for learning and teaching how to use Galaxy for general data analysis, as well as a wide array of hands-on tutorials covering specific domains such as assembly, RNA-Seq analysis, deep learning, climate analysis, and more!
