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---
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title: XGBoost and scikit-learn
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---
The [xgboost_sample.py ](https://github.com/allegroai/clearml/blob/master/examples/frameworks/xgboost/xgboost_sample.py )
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example demonstrates integrating ClearML into code that uses [XGBoost ](https://xgboost.readthedocs.io/en/stable/ ).
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The example does the following:
* Trains a network on the scikit-learn [iris ](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris )
classification dataset using XGBoost
* Scores accuracy using scikit-learn
* ClearML automatically logs the input model registered by XGBoost, and the output model (and its checkpoints),
feature importance plot, and tree plot created with XGBoost.
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* Creates an experiment named `XGBoost simple example` in the `examples` project.
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## Plots
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The feature importance plot and tree plot appear in the project's page in the **ClearML web UI** , under
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**PLOTS**.
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

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## Console
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All other console output appear in **CONSOLE** .
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
## Artifacts
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Models created by the experiment appear in the experiment’ s **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using XGBoost.
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
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Clicking on the model's name takes you to the [model’ s page ](../../../webapp/webapp_model_viewing.md ), where you can
view the model’ s details and access the model.
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