Searching experiment results
MLflow makes it easy to query the results of your experiments, helping you track model performance and hyperparameters.
Let's examine your most recent experiment, finding the model with the lowest Mean Absolute Percentage Error (MAPE).
Este exercício faz parte do curso
Designing Forecasting Pipelines for Production
Instruções do exercício
- Search MLflow runs by the
experiment_name. - Get the single best-performing model from
all_resultsbased onmetrics.mape. - Print the subset of
best_mape_model.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
experiment_name = "hyperparameter_tuning"
# Search MLflow runs
all_results = mlflow.____(experiment_names=[____])
# Filter for the model with the best MAPE score
best_mape_model = all_results.____("metrics.mape").head(____)
# Print the model
print(____[["params.model_name", "metrics.mape"]])