The [xgboost_sample.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/xgboost/xgboost_sample.py)
example demonstrates integrating **ClearML** into code that 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 to do the following:
* Load a model ([xgboost.Booster.load_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.load_model))
* Save a model ([xgboost.Booster.save_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.save_model))
* Dump a model to JSON or text file ([xgboost.Booster.dump_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.dump_model))
* Plot a tree ([xgboost.plot_tree](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.plot_tree))
And using scikit-learn to score accuracy ([sklearn.metrics.accuracy_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html)).
**ClearML** automatically logs:
* Input model
* Output model
* Model checkpoints (snapshots)
* Feature importance plot
* Tree plot
* Output to console.
When the script runs, it 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 **RESULTS****>**
