xgb.plot.shap.summary: SHAP summary plot

In xgboost: Extreme Gradient Boosting

SHAP summary plot

Description

Visualizes 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	The data to explain as a matrix, dgCMatrix, or data.frame.
shap_contrib	Matrix of SHAP contributions of data. The default (NULL) computes it from model and data.
features	Vector of column indices or feature names to plot. When NULL (default), the top_n most important features are selected by xgb.importance().
top_n	How many of the most important features (<= 100) should be selected? By default 1 for SHAP dependence and 10 for SHAP summary. Only used when features = NULL.
model	An xgb.Booster model. Only required when shap_contrib = NULL or features = NULL.
trees	Passed to xgb.importance() when features = NULL.
target_class	Only relevant for multiclass models. The default (NULL) averages the SHAP values over all classes. Pass a (0-based) class index to show only SHAP values of that class.
approxcontrib	Passed to predict.xgb.Booster() when shap_contrib = NULL.
subsample	Fraction of data points randomly picked for plotting. The default (NULL) will use up to 100k data points.

Details

A point plot (each point representing one observation 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 allows 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. Inspired by the summary plot of https://github.com/shap/shap.

Value

A ggplot2 object.

See Also

xgb.plot.shap(), xgb.ggplot.shap.summary(), and the Python library https://github.com/shap/shap.

Examples

# See examples in xgb.plot.shap()

xgboost documentation built on Dec. 3, 2025, 5:06 p.m.