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55 lines
2.4 KiB
Markdown
55 lines
2.4 KiB
Markdown
---
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title: XGBoost
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---
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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 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 to do the following:
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* Load a model ([xgboost.Booster.load_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.load_model))
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* Save a model ([xgboost.Booster.save_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.save_model))
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* 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))
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* Plot feature importance ([xgboost.plot_importance](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.plot_importance))
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* Plot a tree ([xgboost.plot_tree](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.plot_tree))
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And using scikit-learn to score accuracy ([sklearn.metrics.accuracy_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html)).
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**ClearML** automatically logs:
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* Input model
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* Output model
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* Model checkpoints (snapshots)
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* Feature importance plot
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* Tree plot
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* Output to console.
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When the script runs, it creates an experiment named `XGBoost simple example`, which is associated with 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 **RESULTS** **>**
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**PLOTS**.
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## Console
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All other console output appear in **RESULTS** **>** **CONSOLE**.
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
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of the **MODELS** tab.
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The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
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The model info panel contains the model details, including:
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* Model design
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* Label enumeration
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* Model URL
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* Framework.
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