Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

Question

Options

• Amlflow.shap.log_explanation
• BNone of these operations can accomplish the task.
• Cmlflow.shap
• Dmlflow.log_figure
• Eclient.log_artifact

• A
  3% (1)
• B
  6% (2)
• C
  77% (24)
• D
  13% (4)

Explanation

mlflow.shap is MLflow's dedicated integration module for SHAP (SHapley Additive exPlanations), and it is the correct namespace for automatically computing and logging Shapley-based feature importance plots - specifically via mlflow.shap.log_explanation(), which takes a predict function and feature data, computes SHAP values internally, and logs both the values and a summary plot to the active run.

Why the distractors are wrong:

• A (mlflow.shap.log_explanation) appears plausible but is a function within the module, not a standalone operation identifier in the way the question frames it - the module (mlflow.shap) is what "provides" the capability.
• D (mlflow.log_figure) only logs a pre-existing matplotlib/Plotly figure; it does not calculate anything automatically.
• E (client.log_artifact) logs a file path as an artifact - again, no automatic computation of SHAP values.
• B (None of these) is wrong because mlflow.shap absolutely provides this capability.

Memory tip: Think of it as a namespace match - SHAP values → mlflow.shap. Just like mlflow.sklearn handles sklearn model logging, mlflow.shap handles all SHAP-specific operations. If you see "automatically calculate," that signals you need MLflow to do the math, which requires its dedicated integration module, not a generic logging helper.

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