View source: R/shap_aggregated.R
shap_aggregated | R Documentation |
This function works in a similar way to shap function from iBreakDown
but it calculates explanations for a set of observation and then aggregates them.
shap_aggregated(
explainer,
new_observations,
order = NULL,
B = 25,
kernelshap = FALSE,
...
)
explainer |
a model to be explained, preprocessed by the |
new_observations |
a set of new observations with columns that correspond to variables used in the model. |
order |
if not |
B |
number of random paths; works only if kernelshap=FALSE |
kernelshap |
indicates whether the kernelshap method should be used |
... |
other parameters like |
an object of the shap_aggregated
class.
Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://ema.drwhy.ai
library("DALEX")
set.seed(1313)
model_titanic_glm <- glm(survived ~ gender + age + fare,
data = titanic_imputed, family = "binomial")
explain_titanic_glm <- explain(model_titanic_glm,
data = titanic_imputed,
y = titanic_imputed$survived,
label = "glm")
bd_glm <- shap_aggregated(explain_titanic_glm, titanic_imputed[1:10, ])
bd_glm
plot(bd_glm, max_features = 3)
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