--- title: XGBoost and scikit-learn --- The [xgboost_sample.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/xgboost/xgboost_sample.py) example demonstrates integrating ClearML into code that uses [XGBoost](https://xgboost.readthedocs.io/en/stable/). 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. * Creates an experiment named `XGBoost simple example`, which is associated with the `examples` project. ## Plots The feature importance plot and tree plot appear in the project's page in the **ClearML web UI**, under **PLOTS**.  ## Console All other console output appear in **CONSOLE**.  ## Artifacts Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models and any snapshots created using XGBoost.  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. 