R: Prepare data for SHAP plots. To be used in xgb.plot.shap,...

xgb.shap.data {xgboost} | R Documentation

## 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.

[Package xgboost version 1.4.2.1 Index]