f1_score and the actual average_depth of the model. We are interested in models that achieve higher than 0.8 of f1_score and have average_depth lower than 10. It is also a good idea to store other metrics to inspect the models further. For example, we can keep track of each model's inference_time on the test set.f1_score and average_depth capture everything about our problem, we can run a Multimetric Experiment to search for the Pareto Efficient Frontier points. The minimum-performance thresholds can (optionally) be incorporated as Metric Thresholds.optimize strategy with the constraint strategy.get_best_runs method. Notice that the Multimetric experiment finds many dominant points, and an All-Constraint experiment finds more configurations that satisfy the user's constraints.f1_score and lowest average_depth), all models failed to achieve low inference time. All-Constraint experiments recognize that other goals may exist, and they search for a diverse range of outcomes to service future demands. Specifically, note that:inference_time. Notice that only models with low num_boost_round remain active.num_boost_round yields high F1 score -- this is not surprising, but our Multimetric experiment learns this and then spends its energy exploiting that information to make a better Pareto frontier.num_boost_round. That is critical for producing models with good performance and faster inference time.eta (learning rate) values between [-1.5, 0].max_depth than Multimetric, especially between values 5 and 15.min_child_weight values seems to produce acceptable results -- the metrics seem unaffected by this parameter alone. However, for satisfactory models, it looks like max_depth and min_child_weight are inversely correlated.budget must be set when a All-Constraint experiment is created.