shap.prep.stack.data: Prepare data for SHAP force plot (stack plot)

Description Usage Arguments Value Examples

Description

Make force plot for top_n features, optional to randomly plot certain portion of the data in case the dataset is large.

Usage

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shap.prep.stack.data(
  shap_contrib,
  top_n = NULL,
  data_percent = 1,
  cluster_method = "ward.D",
  n_groups = 10L
)

Arguments

shap_contrib

shap_contrib is the SHAP value data returned from predict, here an ID variable is added for each observation in the shap_contrib dataset for better tracking, it is created in the begining as 1:nrow(shap_contrib). The ID matches the output from shap.prep

top_n

integer, optional to show only top_n features, combine the rest

data_percent

what percent of data to plot (to speed up the testing plot). The accepted input range is (0,1], if observations left is too few, there will be an error from the clustering function

cluster_method

default to ward.D, please refer to stats::hclust for details

n_groups

a integer, how many groups to plot in shap.plot.force_plot_bygroup

Value

a dataset for stack plot

Examples

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# **SHAP force plot**
plot_data <- shap.prep.stack.data(shap_contrib = shap_values_iris,
                                  n_groups = 4)
shap.plot.force_plot(plot_data)
shap.plot.force_plot(plot_data,  zoom_in_group = 2)

# plot all the clusters:
shap.plot.force_plot_bygroup(plot_data)

SHAPforxgboost documentation built on March 28, 2021, 9:06 a.m.