XGBoost

Try in a Colab Notebook here →

The wandb library has a WandbCallback callback for logging metrics, configs and saved boosters from training with XGBoost. Here you can see a live Weights & Biases dashboard with outputs from the XGBoost WandbCallback.

Get Started

Logging XGBoost metrics, configs and booster models to Weights & Biases is as easy as passing the WandbCallback to XGBoost:

from wandb.integration.xgboost import WandbCallback
import xgboost as XGBClassifier

...
# Start a wandb run
run = wandb.init()

# Pass WandbCallback to the model
bst = XGBClassifier()
bst.fit(X_train, y_train, callbacks=[WandbCallback(log_model=True)])

# Close your wandb run
run.finish()

You can open this notebook for a comprehensive look at logging with XGBoost and Weights & Biases

WandbCallback

Functionality

Passing WandbCallback to a XGBoost model will:

• log the booster model configuration to Weights & Biases
• log evaluation metrics collected by XGBoost, such as rmse, accuracy etc to Weights & Biases
• log training metrics collected by XGBoost (if you provide data to eval_set)
• log the best score and the best iteration
• save and upload your trained model to to Weights & Biases Artifacts (when log_model = True)
• log feature importance plot when log_feature_importance=True (default).
• Capture the best eval metric in wandb.summary when define_metric=True (default).

Arguments

log_model: (boolean) if True save and upload the model to Weights & Biases Artifacts

log_feature_importance: (boolean) if True log a feature importance bar plot

importance_type: (str) one of {weight, gain, cover, total_gain, total_cover} for tree model. weight for linear model.

define_metric: (boolean) if True (default) capture model performance at the best step, instead of the last step, of training in your wandb.summary.

You can find the source code for WandbCallback here

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Looking for more working code examples? Check out our repository of examples on GitHub or try out a Colab notebook

Tuning your hyperparameters with Sweeps

Attaining the maximum performance out of models requires tuning hyperparameters, like tree depth and learning rate. Weights & Biases includes Sweeps, a powerful toolkit for configuring, orchestrating, and analyzing large hyperparameter testing experiments.

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To learn more about these tools and see an example of how to use Sweeps with XGBoost, check out this interactive Colab notebook or try this XGBoost & Sweeps python script here