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).
Questo esercizio fa parte del corso
Designing Forecasting Pipelines for Production
Istruzioni dell'esercizio
- 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.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
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"]])