mirror of
https://github.com/clearml/clearml-docs
synced 2025-02-26 05:59:41 +00:00
42 lines
1.6 KiB
Markdown
42 lines
1.6 KiB
Markdown
---
|
||
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.
|
||
|
||
 |