Learn how SHAP breaks a single prediction into feature contributions you can trust. Understand common pitfalls like correlated features and baseline choices.
SHAP values explain a single prediction by allocating the difference from a baseline to features based on Shapley values.
They sum to the model’s training accuracy
They sum to the model output minus the baseline for that instance
They always equal global feature importance scores
They are the same for every instance in a class
What does a negative SHAP value for a feature indicate for a given prediction?
The feature is unimportant globally
The feature has missing values
The feature pushed the prediction lower relative to the baseline
The feature has data leakage
Why does the choice of background or baseline dataset matter when using SHAP?
It defines the reference from which contributions are measured
It guarantees independence among features
It changes the model’s weights during training
It eliminates the need for cross-validation
How should SHAP be used to understand overall drivers, not just single cases?
Use only the largest SHAP value from one instance
Aggregate per-instance SHAP values across many predictions
Convert SHAP values into class probabilities
Average raw feature values instead
When features are highly correlated, SHAP attributions can appear diluted or unstable because ______.
correlation forces all SHAP values to be zero
credit is split among correlated features depending on the background distribution
SHAP ignores correlated features entirely
the model cannot learn with correlated features
Tree-based models often use TreeSHAP because it is ______ for many tree ensembles.
computationally efficient and exact under model assumptions
incompatible with gradient-boosted trees
a heuristic that ignores the model structure
only approximate for linear models
Which statement best contrasts SHAP with permutation importance?
SHAP is local per instance; permutation importance is global and model-agnostic
Both are strictly global metrics only
Permutation importance produces signed contributions per instance
SHAP requires retraining the model each time
What are SHAP interaction values meant to capture?
The impact of class imbalance
Variance due to random seeds
Only the effect of regularization on weights
How pairs of features jointly affect a prediction beyond their main effects
Which practice makes SHAP-based monitoring more reliable over time?
Compare distributions of SHAP values over consistent cohorts or windows
Switch the baseline dataset every day
Only monitor features with zero variance
Remove all negative SHAP values
If SHAP results look counterintuitive, what is a sensible first check?
Verify preprocessing and feature definitions match what the model actually sees
Switch from SHAP to accuracy-only evaluation
Discard the feature with the largest SHAP value
Assume the model is overfitted and retrain immediately
Starter
You’re learning how per-instance attributions work. Review baselines and correlated features.
Solid
Nice grasp of SHAP mechanics. Deepen by exploring interaction values and cohort summaries.
Expert!
Masterful! You interpret SHAP with nuance and communicate insights clearly.
