Pin xgboost in recipes tests

[harupy]

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

See microsoft/FLAML#1217 for details. Fixes https://github.com/mlflow/mlflow/actions/runs/6156373110/job/16704908108?pr=9603:

Traceback (most recent call last):
  File "/home/runner/work/mlflow/mlflow/mlflow/recipes/steps/automl/flaml.py", line 170, in _create_model_automl
    automl.fit(X, y, **automl_settings)
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/automl.py", line 1928, in fit
    self._search()
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/automl.py", line 2482, in _search
    self._search_sequential()
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/automl.py", line 2318, in _search_sequential
    analysis = tune.run(
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/tune/tune.py", line 808, in run
    result = evaluation_function(trial_to_run.config)
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/state.py", line 302, in _compute_with_config_base
    ) = compute_estimator(
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/ml.py", line 369, in compute_estimator
    val_loss, metric_for_logging, train_time, pred_time = task.evaluate_model_CV(
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/task/generic_task.py", line 737, in evaluate_model_CV
    val_loss_i, metric_i, train_time_i, pred_time_i = get_val_loss(
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/ml.py", line 494, in get_val_loss
    estimator.fit(X_train, y_train, budget=budget, free_mem_ratio=free_mem_ratio, **fit_kwargs)
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/model.py", line 1652, in fit
    return super().fit(X_train, y_train, budget, free_mem_ratio, **kwargs)
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/flaml/automl/model.py", line 1415, in fit
    self._model.get_booster().best_iteration
  File "/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/xgboost/core.py", line 2602, in best_iteration
    raise AttributeError(
AttributeError: `best_iteration` is only defined when early stopping is used.

How is this patch tested?

• Existing unit/integration tests
• New unit/integration tests
• Manual tests (describe details, including test results, below)

Does this PR require documentation update?

• No. You can skip the rest of this section.
• Yes. I've updated:
  • Examples
  • API references
  • Instructions

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/gateway: AI Gateway service, Gateway client APIs, third-party Gateway integrations
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templates
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

[WeichenXu123]

LGTM

[github-actions]

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