Survival analysis is a task dealing with time-to-event prediction. Aside from the well-understood models like CPH, many more complex models have recently emerged, but most lack interpretability. Due to a functional type of prediction, either in the form of survival function or cumulative hazard function, standard model-agnostic explanations cannot be applied directly.
The survex package provides model-agnostic explanations
for machine learning survival models. It is based on the DALEX
package. If you’re unfamiliar with explainable machine learning,
consider referring to the Explanatory
Model Analysis book – most of the methods included in
survex extend these described in EMA and implemented in
DALEX but to models with functional output.
The main explain() function uses a model and data to
create a standardized explainer object, which is further
used as an interface for calculating predictions. We automate creating
explainers from the following packages: mlr3proba,
censored, ranger,
randomForestSRC, and survival. Raise
an Issue on GitHub if you find models from other packages that we can
incorporate into the explain() interface.
Note that an explainer can be created for any
survival model, using the explain_survival() function by
passing model, data, y, and
predict_survival_function arguments.
The package is available on CRAN:
install.packages("survex")The latest development version can be installed from GitHub using
devtools::install_github():
devtools::install_github("https://github.com/ModelOriented/survex")library("survex")
library("survival")
library("ranger")
# create a model
model <- ranger(Surv(time, status) ~ ., data = veteran)
# create an explainer
explainer <- explain(model,
data = veteran[, -c(3, 4)],
y = Surv(veteran$time, veteran$status))
# evaluate the model
model_performance(explainer)
# visualize permutation-based feature importance
plot(model_parts(explainer))
# explain one prediction with SurvSHAP(t)
plot(predict_parts(explainer, veteran[1, -c(3, 4)]))