xgb.plot.shap.summary: SHAP contribution dependency summary plot

In xgboost: Extreme Gradient Boosting

SHAP contribution dependency summary plot

Description

Compare SHAP contributions of different features.

Usage

xgb.ggplot.shap.summary(
  data,
  shap_contrib = NULL,
  features = NULL,
  top_n = 10,
  model = NULL,
  trees = NULL,
  target_class = NULL,
  approxcontrib = FALSE,
  subsample = NULL
)

xgb.plot.shap.summary(
  data,
  shap_contrib = NULL,
  features = NULL,
  top_n = 10,
  model = NULL,
  trees = NULL,
  target_class = NULL,
  approxcontrib = FALSE,
  subsample = NULL
)

Arguments

data	data as a matrix or dgCMatrix.
shap_contrib	a matrix of SHAP contributions that was computed earlier for the above data. When it is NULL, it is computed internally using model and data.
features	a vector of either column indices or of feature names to plot. When it is NULL, feature importance is calculated, and top_n high ranked features are taken.
top_n	when features is NULL, top_n [1, 100] most important features in a model are taken.
model	an xgb.Booster model. It has to be provided when either shap_contrib or features is missing.
trees	passed to xgb.importance when features = NULL.
target_class	is only relevant for multiclass models. When it is set to a 0-based class index, only SHAP contributions for that specific class are used. If it is not set, SHAP importances are averaged over all classes.
approxcontrib	passed to predict.xgb.Booster when shap_contrib = NULL.
subsample	a random fraction of data points to use for plotting. When it is NULL, it is set so that up to 100K data points are used.

Details

A point plot (each point representing one sample from data) is produced for each feature, with the points plotted on the SHAP value axis. Each point (observation) is coloured based on its feature value. The plot hence allows us to see which features have a negative / positive contribution on the model prediction, and whether the contribution is different for larger or smaller values of the feature. We effectively try to replicate the summary_plot function from https://github.com/slundberg/shap.

Value

A ggplot2 object.

See Also

xgb.plot.shap, xgb.ggplot.shap.summary, https://github.com/slundberg/shap

Examples

# See \code{\link{xgb.plot.shap}}.

xgboost documentation built on March 31, 2023, 10:05 p.m.