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xgboost

xgb.shap.data

Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc. Internal utility function.

Description

Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc. Internal utility function.

Usage

xgb.shap.data(
  data,
  shap_contrib = NULL,
  features = NULL,
  top_n = 1,
  model = NULL,
  trees = NULL,
  target_class = NULL,
  approxcontrib = FALSE,
  subsample = NULL,
  max_observations = 1e+05
)

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.

Value

A list containing: 'data', a matrix containing sample observations and their feature values; 'shap_contrib', a matrix containing the SHAP contribution values for these observations.

xgboost

Extreme Gradient Boosting

v1.4.1.1

Apache License (== 2.0) | file LICENSE

Authors

Tianqi Chen [aut], Tong He [aut, cre], Michael Benesty [aut], Vadim Khotilovich [aut], Yuan Tang [aut] (<https://orcid.org/0000-0001-5243-233X>), Hyunsu Cho [aut], Kailong Chen [aut], Rory Mitchell [aut], Ignacio Cano [aut], Tianyi Zhou [aut], Mu Li [aut], Junyuan Xie [aut], Min Lin [aut], Yifeng Geng [aut], Yutian Li [aut], XGBoost contributors [cph] (base XGBoost implementation)

Initial release

2021-04-22

xgb.shap.data

Description

Usage

Arguments

Value

xgboost

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