shap_var: SHAP-based dependence plots for categorical/numerical...

In lares: Analytics & Machine Learning Sidekick

SHAP-based dependence plots for categorical/numerical features (PDP)

Description

Having a h2o_shap object, plot a dependence plot for any categorical or numerical feature.

Usage

shap_var(x, var, keep_outliers = FALSE)

Arguments

x	h2o_shap object
var	Variable name
keep_outliers	Boolean. Outliers detected with z-score and 3sd may be suppress or kept in your plot. Keep them?

Value

ggplot2 objct with shap values plotted

See Also

Other SHAP: h2o_shap()

Examples

## Not run: 
# Train a h2o_automl model
model <- h2o_automl(dft, Survived,
  max_models = 1, target = TRUE,
  ignore = c("Ticket", "Cabin", "PassengerId"),
  quiet = TRUE
)

# Calculate SHAP values
SHAP_values <- h2o_shap(model)
# Equivalent to:
# SHAP_values <- h2o_shap(
#  model = model$model,
#  test = model$datasets$test,
#  scores = model$scores_test$scores)

# Check SHAP results
head(SHAP_values)

# You must have "ggbeeswarm" library to use this auxiliary function:
# Plot SHAP values (feature importance)
plot(SHAP_values)

# Plot some of the variables (categorical)
shap_var(SHAP_values, Pclass)

# Plot some of the variables (numerical)
shap_var(SHAP_values, Fare)

## End(Not run)

lares documentation built on Sept. 13, 2024, 1:08 a.m.