Automatically track XGBoost training progress to Neptune
- Log XGBoost metrics to Neptune (train and eval)
- Log model, feature importance and trees visualizations for deeper analysis
- Compare across multiple experiments
XGBoost metrics logged to Neptune automatically
Experiment tracking tool with XGBoost integration, to automatically log training and validation progress.
cv functions in a regular way. If you prefer the sklearn API it’s fine too. Then, simply add
neptune_callback that will log metrics, model, feature importance charts and visualized trees out-of-the-box.
- Log metrics, text, images, files and more to the experiments.
- Log experiment parameters.
- Easy integration with Python libraries.
- All charts visible for team members.
- Compare across multiple experiments and gain insight.
- Download charts from UI.
Backed up experiments history
- All visualizations are stored and secured.
- Keep all history – review when needed.
- Secured Intellectual Property (IP).
Register and try it out!