pred_nestcv_glmnet function

Prediction wrappers to use fastshap with nestedcv

Prediction wrapper functions to enable the use of the fastshap package for generating SHAP values from nestedcv trained models.

pred_nestcv_glmnet(x, newdata)

pred_nestcv_glmnet_class(cl)

pred_train(x, newdata)

pred_train_class(cl)

pred_SuperLearner(x, newdata)

Arguments

• x: a nestcv.glmnet or nestcv.train object
• newdata: a matrix of new data
• cl: integer representing which class to predict

Returns

prediction wrapper function designed for use with fastshap::explain()

Details

These prediction wrapper functions are designed to be used with the fastshap package. The functions pred_nestcv_glmnet and pred_train work for nestcv.glmnet and nestcv.train models respectively for either binary classification or regression.

For multiclass classification use pred_nestcv_glmnet_class(1), pred_nestcv_glmnet_class(2) etc for each class. Similarly pred_train_class(1), pred_train_class(2) etc for nestcv.train objects.

Examples

library(fastshap)

# Boston housing dataset
library(mlbench)
data(BostonHousing2)
dat <- BostonHousing2
y <- dat$cmedv
x <- subset(dat, select = -c(cmedv, medv, town, chas))

# Fit a glmnet model using nested CV
# Only 3 outer CV folds and 1 alpha value for speed
fit <- nestcv.glmnet(y, x, family = "gaussian", n_outer_folds = 3, alphaSet = 1)

# Generate SHAP values using fastshap::explain
# Only using 5 repeats here for speed, but recommend higher values of nsim
sh <- explain(fit, X=x, pred_wrapper = pred_nestcv_glmnet, nsim = 1)

# Plot overall variable importance
plot_shap_bar(sh, x)

# Plot beeswarm plot
plot_shap_beeswarm(sh, x, size = 1)