Can someone explain the concept of Shapley value masking when working with tabular data and classification problems? My understanding of a mask in arrays is to have boolean values matching the shape of a query array (n, m) where you would mask the query and perform an operation. For example, this operation to sum only the positive values in an array:
import numpy as np
query = np.random.normal(size=(5,10))
mask = query > 0
np.sum(query[mask], axis=1)


*

*Is the concept of masking in SHAP similar, the same, or completely different? I'm talking exclusively about tabular data.

*How do you decide on a mask to make the process reproducible and not subjective?

Let's say you have the following conditions:
* Binary classification: Healthy, Disease
* Multiclass classification: Healthy, Disease_A, Disease_B (mutually exclusive, can only be one per sample)



*In a lot of the examples, they are masking a training set and then calculating SHAP values on the test set.  This seems to be dependent on the random seed.  Would it be better to use cross-validation and calculate a distribution of SHAP values per feature?

For example, like this:
for training_index, testing_index in KFold(n_splits=3, random_state=0, shuffle=True).split(X, y):
    X_training = X.iloc[training_index]
    X_testing = X.iloc[testing_index]
    explainer = shap.KernelExplainer(clf.predict_proba, X_training)
    shap_values = explainer.shap_values(X_testing)



*Last question, how can I calculate a "final" SHAP value for my dataset given all of the data?  Either a single vector that has the total predictive capacity of a feature or an array (m features, c classes).

 A: *

*ML Models usually accept inputs with fixed number of features: If model was trained on 10 features, it is impossible to provide a point where only 7 features are defined.

SHAP operates on a different coalition of features, so to compute a feature importance (SHAP value) for feature f1, it checks what model predicts if only f1 defined, if (f1, f2) are defined, (f1, f3) and so on, (see how Shapley Values are defined for more information).
As I mentioned for ML models absent features still must be defined somehow. zero, NaN or any other values can used for example, but such values may produce out-of-distribution samples: if the model have never seen zero for a feature it may go crazy. To cope with that a masker/background dataset can be used. Suppose there is a point $x$, and we need to evaluate model on features $f_1, f_2$, and "ignore" features $f_3, f_4$. To do that the absent features are substituted with every points from the masker dataset, and we have a new dataset $D$ with the same size as masker dataset where $f_1, f_2$ features are always the same and equal $x$, and $f_3, f_4$ equal to masker dataset. Then we evaluate model on $D$ and take the average.


*I think is a good idea to cross-validate it on training (masker) dataset, for training dataset it depends if local or global Shapley values should be obtained


*If "final" is a global shapley values -- average shapley values for different point is a standard solution.
