Machine learning pipeline in 3D
Follow data splitting, model fitting, validation and held-out testing. Explore the 3D model, inspect its parts and learn with guided explanations. Free on Atlas.
What you can learn
Follow data splitting, model fitting, validation and held-out testing.
Protect the held-out data.
Fit transformations and a model.
Use validation to make choices.
Try an investigation
Trace data split toward fitted preprocessing example. Which intermediate operations or assemblies are essential, and which assumptions are simplified by this diagram?
Check your understanding
When should data splitting happen?
Before fitting learned preprocessing. Before fitting learned preprocessing. The exhibit shows computational roles and dependencies; it does not contain trained parameters or run inference.
What selects development settings?
Validation feedback. Validation feedback. The exhibit shows computational roles and dependencies; it does not contain trained parameters or run inference.
New examples should use…
The fitted preprocessing pipeline. The fitted preprocessing pipeline. The exhibit shows computational roles and dependencies; it does not contain trained parameters or run inference.
Sources and model scope
A three-dimensional computational diagram, not physical hardware or a running trained model. Displayed widths, node counts and example values are illustrative. Select named stages to trace information flow. The separate upper study rail contains deterministic worked examples, with values on every selectable cell.