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47 lines
2.1 KiB
Markdown
47 lines
2.1 KiB
Markdown
---
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title: XGBoost and scikit-learn
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---
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The [xgboost_sample.py](https://github.com/clearml/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:
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* Trains a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris)
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classification dataset using XGBoost
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* Scores accuracy using scikit-learn
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* ClearML automatically logs the input model registered by XGBoost, and the output model (and its checkpoints),
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feature importance plot, and tree plot created with XGBoost.
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* Creates a task 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 task's page in the **ClearML web UI**, under
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**PLOTS**.
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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 task appear in the task's **ARTIFACTS** tab. ClearML automatically logs and tracks
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models and any snapshots created using XGBoost.
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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
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view the model's details and access the model.
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