2.4 KiB
| title |
|---|
| XGBoost |
The xgboost_sample.py example demonstrates integrating ClearML into code that trains a network on the scikit-learn iris classification dataset, using XGBoost to do the following:
- Load a model (xgboost.Booster.load_model)
- Save a model (xgboost.Booster.save_model)
- Dump a model to JSON or text file (xgboost.Booster.dump_model)
- Plot feature importance (xgboost.plot_importance)
- Plot a tree (xgboost.plot_tree)
And using scikit-learn to score accuracy (sklearn.metrics.accuracy_score).
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 > PLOTS.
Console
All other console output appear in RESULTS > CONSOLE.
Artifacts
Model artifacts associated with the experiment appear in the info panel of the EXPERIMENTS tab and in the info panel of the MODELS tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
The model info panel contains the model details, including:
- Model design
- Label enumeration
- Model URL
- Framework.
