83 lines
2.6 KiB
R
83 lines
2.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.shap.R
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\name{xgb.ggplot.shap.summary}
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\alias{xgb.ggplot.shap.summary}
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\alias{xgb.plot.shap.summary}
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\title{SHAP summary plot}
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\usage{
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xgb.ggplot.shap.summary(
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data,
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shap_contrib = NULL,
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features = NULL,
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top_n = 10,
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model = NULL,
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trees = NULL,
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target_class = NULL,
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approxcontrib = FALSE,
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subsample = NULL
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)
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xgb.plot.shap.summary(
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data,
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shap_contrib = NULL,
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features = NULL,
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top_n = 10,
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model = NULL,
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trees = NULL,
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target_class = NULL,
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approxcontrib = FALSE,
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subsample = NULL
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)
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}
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\arguments{
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\item{data}{The data to explain as a \code{matrix}, \code{dgCMatrix}, or \code{data.frame}.}
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\item{shap_contrib}{Matrix of SHAP contributions of \code{data}.
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The default (\code{NULL}) computes it from \code{model} and \code{data}.}
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\item{features}{Vector of column indices or feature names to plot. When \code{NULL}
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(default), the \code{top_n} most important features are selected by \code{\link[=xgb.importance]{xgb.importance()}}.}
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\item{top_n}{How many of the most important features (<= 100) should be selected?
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By default 1 for SHAP dependence and 10 for SHAP summary.
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Only used when \code{features = NULL}.}
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\item{model}{An \code{xgb.Booster} model. Only required when \code{shap_contrib = NULL} or
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\code{features = NULL}.}
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\item{trees}{Passed to \code{\link[=xgb.importance]{xgb.importance()}} when \code{features = NULL}.}
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\item{target_class}{Only relevant for multiclass models. The default (\code{NULL})
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averages the SHAP values over all classes. Pass a (0-based) class index
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to show only SHAP values of that class.}
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\item{approxcontrib}{Passed to \code{predict()} when \code{shap_contrib = NULL}.}
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\item{subsample}{Fraction of data points randomly picked for plotting.
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The default (\code{NULL}) will use up to 100k data points.}
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}
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\value{
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A \code{ggplot2} object.
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}
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\description{
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Visualizes SHAP contributions of different features.
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}
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\details{
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A point plot (each point representing one observation from \code{data}) is
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produced for each feature, with the points plotted on the SHAP value axis.
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Each point (observation) is coloured based on its feature value.
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The plot allows to see which features have a negative / positive contribution
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on the model prediction, and whether the contribution is different for larger
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or smaller values of the feature. Inspired by the summary plot of
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\url{https://github.com/shap/shap}.
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}
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\examples{
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# See examples in xgb.plot.shap()
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}
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\seealso{
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\code{\link[=xgb.plot.shap]{xgb.plot.shap()}}, \code{\link[=xgb.ggplot.shap.summary]{xgb.ggplot.shap.summary()}},
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and the Python library \url{https://github.com/shap/shap}.
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}
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