This example trains an XGBoost classifier with the iris dataset and logs hyperparameters, metrics, and trained model.
python train.py --learning-rate 0.2 --colsample-bytree 0.8 --subsample 0.9
You can try experimenting with different parameter values like:
python train.py --learning-rate 0.4 --colsample-bytree 0.7 --subsample 0.8
Then you can open the MLflow UI to track the experiments and compare your runs via:
mlflow ui
mlflow run . -P learning_rate=0.2 -P colsample_bytree=0.8 -P subsample=0.9