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SHAP summary plot

Source: R/xgb.ggplot.R, R/xgb.plot.shap.R
xgb.plot.shap.summary.Rd

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.

Value

A ggplot2 object.

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.

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()