shapley (version 0.1)

shapley.top: Select top features in a model

Description

This function applies different criteria simultaniously to identify the most important features in a model. The criteria include: 1) minimum limit of lower weighted confidence intervals of SHAP values relative to the feature with highest SHAP value. 2) minimum limit of percentage of weighted mean SHAP values relative to over all SHAP values of all features. These are specified with two different cutoff values.

Usage

shapley.top(shapley, lowerci = 0.01, shapratio = 0.005)

Value

data.frame of selected features

Arguments

shapley

object of class 'shapley', as returned by the 'shapley' function

lowerci

numeric, specifying the lower limit of weighted confidence intervals of SHAP values relative to the feature with highest SHAP value. the default is 0.01

shapratio

numeric, specifying the lower limit of percentage of weighted mean SHAP values relative to over all SHAP values of all features. the default is 0.005

Author

E. F. Haghish

Examples

if (FALSE) {
# load the required libraries for building the base-learners and the ensemble models
library(h2o)            #shapley supports h2o models
library(shapley)

# initiate the h2o server
h2o.init(ignore_config = TRUE, nthreads = 2, bind_to_localhost = FALSE, insecure = TRUE)

# upload data to h2o cloud
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.importFile(path = prostate_path, header = TRUE)

### H2O provides 2 types of grid search for tuning the models, which are
### AutoML and Grid. Below, I demonstrate how weighted mean shapley values
### can be computed for both types.

set.seed(10)

#######################################################
### PREPARE AutoML Grid (takes a couple of minutes)
#######################################################
# run AutoML to tune various models (GBM) for 60 seconds
y <- "CAPSULE"
prostate[,y] <- as.factor(prostate[,y])  #convert to factor for classification
aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 120,
                 include_algos=c("GBM"),

                 # this setting ensures the models are comparable for building a meta learner
                 seed = 2023, nfolds = 10,
                 keep_cross_validation_predictions = TRUE)

### call 'shapley' function to compute the weighted mean and weighted confidence intervals
### of SHAP values across all trained models.
### Note that the 'newdata' should be the testing dataset!
result <- shapley(models = aml, newdata = prostate, plot = TRUE)

#######################################################
### Significance testing of contributions of two features
#######################################################

shapley.top(result, lowerci = 0.01, shapratio = 0.005)
}