> For the complete documentation index, see [llms.txt](https://medomicslab.gitbook.io/medomics-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://medomicslab.gitbook.io/medomics-docs/tutorials/development/learning-module/experimental-scene.md). # Experimental Scene This page covers everything you need to know about the experimental scene and how it can be used as an initial testing environment. This new scene type applies the same [new design](/medomics-docs/tutorials/development/learning-module.md#a-new-design-is-here) discussed earlier, but reduces the nodes available while maintaining full experimental capabilities: * [**Dataset node**](/medomics-docs/tutorials/development/learning-module/initialization.md#dataset-node-your-experiments-starting-point) (previously discussed in Initialization). * [**Clean node**](/medomics-docs/tutorials/development/learning-module/initialization.md#clean-node-tidy-and-transform-your-data) (previously discussed in Initialization). * **Compare models node**: Train and evaluate the performance of all estimators available in the [model library](https://pycaret.gitbook.io/docs/get-started/functions/train#model-library).

Key differences between the Main and the Experimental scene

This setup provides a dedicated testing environment for machine learning experiments, allowing users to evaluate and select the optimal algorithm for their task while having initial performance estimates that can be leveraged to guide the main experiments, as illustrated in the new machine learning workflow.

### Compare Models Node: Only available in Experimental Mode and used in the Training Box, the Compare Models node lets you efficiently train and evaluate multiple machine learning estimators simultaneously, making it easy to identify the best performer for your task. While offering numerous configuration options for advanced users, the node requires no mandatory settings, and you can start comparing different machine learning algorithms immediately after connecting the node. Note that the Compare Models node is based on [PyCaret's function *`compare_models()`*](https://pycaret.readthedocs.io/en/stable/api/classification.html#pycaret.classification.compare_models).

The results of the Compare Models node contain the complete set of performance metrics for all the machine learning algorithms tested:

{% hint style="danger" %} Note that in Experimental mode, the models are trained and tested using a single iteration, resulting in a low bias but a high variance (uncertainty). {% endhint %} The final section outlines key practices to avoid for optimal performance and reliable execution within the learning module. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://medomicslab.gitbook.io/medomics-docs/tutorials/development/learning-module/experimental-scene.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.