{% hint style="info" %}
Before proceeding to *Step 7 - Evaluate & Apply Model*, you will need to download the models folder (*MEDomicsLab\_TestingPhase\_Step7.zip*) into your *EXPERIMENTS* folder. This folder contains the models we prepared for you. One of the models corresponds to the model you created during [*Step 6 - Create Model*](https://medomics-udes.gitbook.io/medomicslab-docs/test-with-mimic/step-6). However, for consistency across all participants of the Testing Phase, we recommend using the model we provided specifically for *Step 7 - Evaluate & Apply Model*.
Additionally, for this step, you will also need the data we sent you for [*Step 6 - Create Model*](https://medomics-udes.gitbook.io/medomicslab-docs/test-with-mimic/step-6) (*MEDomicsLab\_TestingPhase\_Step6.zip*), which contains the holdout patient set we will use to evaluate our models.
An invitation to access the *MEDomicsLab\_TestingPhase\_Step7.zip* data has been sent to you via email.
{% endhint %}
In this current *Step 7 - Evaluate & Apply Model*, we will explore the functionalities of the [*Evaluation Module*](https://medomics-udes.gitbook.io/medomicslab-docs/tutorials/development/evaluation-module) by evaluating two machine learning models on our holdout set. As the models were created using only *Time point 1* and *Time point 2* from the learning set (see [*Step 6 - Create Model*](https://medomics-udes.gitbook.io/medomicslab-docs/test-with-mimic/step-6) for more details), we are going to evaluate them on *Time point 1* and *Time point 2* from the holdout set.
Additionally, we will explore the functionalities of the [*Application Module*](https://medomics-udes.gitbook.io/medomicslab-docs/tutorials/deployment/application-module) by applying one of the models to a single patient from the holdout set.
**Model 1: ExtraTrees Classifier**
Documentation related to this model is available [here](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html). In our experiment, we kept the model's default values.
This model is the one we obtained at the end of [*Step 6 - Create Model*](https://medomics-udes.gitbook.io/medomicslab-docs/test-with-mimic/step-6). It is trained on *Time point 1* and *Time point 2* from our learning set, using the following columns (T1 and T2 suffix refers to T1 and T2 datasets):
* tslab\_|\_attr\_MCHC\_\_maximum\_T1
* nradiology\_|\_attr4\_T1
* nradiology\_|\_attr6\_T1
* image\_|\_attr5(3)\_T1
* image\_|\_attr7(3)\_T1
* demographics\_|\_anchor\_age\_T1
* nradiology\_|\_attr4\_T2
* nradiology\_|\_attr6\_T2
* tslab\_|\_attr\_Platelet\_Count\_\_mean\_T2
* tslab\_|\_attr\_MCHC\_\_maximum\_T2
* tslab\_|\_attr\_MCH\_\_maximum\_T2
* image\_|\_attr5(3)\_T2
* image\_|\_attr7(3)\_T2
**Model 2: Random Forest Classifier**
Documentation related to this model is available [here](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). In our experiment, we kept the model's default values.
We created this model specifically for this step. Like the previous model, it was trained on *Time point 1* and *Time point 2* from the learning set, using the same columns.
**Apply Model**
This section involves selecting a model and applying it to a new patient. You will need to enter the patient's required information, based on the columns the model was trained on, and observe the prediction the model makes for this new patient.
{% hint style="info" %}
Please note that the [*Evaluation Module*](https://medomics-udes.gitbook.io/medomicslab-docs/tutorials/development/evaluation-module) dashboard is a basic implementation of the [*ExplainerDashboard* Python library](https://explainerdashboard.readthedocs.io/en/latest/index.html). Additionally, if you are seeking information about the dashboard elements, you may find it in the [*ExplainerDashboard* documentation](https://explainerdashboard.readthedocs.io/en/latest/index.html).
Also, if you want to fully understand how *ExplainerDashboard* works in the background, it is an open-source library, and the code is available on [GitHub](https://github.com/oegedijk/explainerdashboard/tree/master).
{% endhint %}
## Recommendations
Before proceeding with *Step 7 - Evaluate & Apply Mode*l of the MEDomicsLab Testing Phase, we recommend consulting the documentation of the [*Evaluation Module*](https://medomics-udes.gitbook.io/medomicslab-docs/tutorials/development/evaluation-module) and the [*Application Module*](https://medomics-udes.gitbook.io/medomicslab-docs/tutorials/deployment/application-module).
{% content-ref url="/pages/qJN7LVnGfw4SFoiKzeyL" %}
[Evaluation Module](/medomics-docs/medomicslab-docs-v0/tutorials/development/evaluation-module.md)
{% endcontent-ref %}
{% content-ref url="/pages/1pn0dfKvZikGhGtTHzrS" %}
[Application Module](/medomics-docs/medomicslab-docs-v0/tutorials/deployment/application-module.md)
{% endcontent-ref %}
## Instructions for Step 7 - Evaluate & Apply Model
{% embed url="" %}
**Content**
Intro [0:00](https://www.youtube.com/watch?v=klxuMgQWI00\&t=0s)
Evaluate 1st model [2:25](https://www.youtube.com/watch?v=klxuMgQWI00\&t=145s)
Evaluate 2nd model [14:59](https://www.youtube.com/watch?v=klxuMgQWI00\&t=899s)
Apply model [18:19](https://www.youtube.com/watch?v=klxuMgQWI00\&t=1099s)
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://medomicslab.gitbook.io/medomics-docs/medomicslab-docs-v0/test-with-mimic/step-7.md?ask=
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The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
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