Working with XGBoost

XGBoost is a popular machine learning library that is used for building gradient boosting models. It is known for its performance and computational speed. Here, we will discuss how to use XGBoost with Azure Machine Learning.

Using XGBoost with Azure Machine Learning

Azure Machine Learning provides support for XGBoost through its SDK. You can use XGBoost for various tasks such as classification, regression, and ranking tasks. Here are some examples of how to use XGBoost with Azure Machine Learning.

import argparse
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, log_loss
import xgboost as xgb
import mlflow
import mlflow.xgboost

# ... rest of the code ...

Dependencies

To use XGBoost with Azure Machine Learning, you need to install the following Python packages:

• xgboost

• mlflow

• azureml-mlflow

• scikit-learn

• pandas

• numpy

• matplotlib

You can install these packages using pip:

pip install xgboost mlflow azureml-mlflow scikit-learn pandas numpy matplotlib

Training a Model

You can train a XGBoost model using the xgb.train function. Here is an example:

# ... rest of the code ...

# train model
params = {
    "objective": "multi:softprob",
    "num_class": 3,
    "learning_rate": args.learning_rate,
    "eval_metric": "mlogloss",
    "colsample_bytree": args.colsample_bytree,
    "subsample": args.subsample,
    "seed": 42,
}

if args.compute == "CPU":
    params.update({"tree_method": "hist"})
else:
    params.update({"tree_method": "gpu_hist"})

model = xgb.train(params, dtrain, evals=[(dtrain, "train")])

# ... rest of the code ...

Logging and Customizing Models

You can log and customize your XGBoost models using MLflow. Here is an example:

# ... rest of the code ...

# log metrics
mlflow.log_metrics({"log_loss": loss, "accuracy": acc})

# ... rest of the code ...

For more information, please refer to the Azure Machine Learning documentation.