Description Usage Arguments Value Examples
This function prints the feature impact breakdown for a single data row, and plots an accompanying waterfall chart.
1 2 | showWaterfall(xgb.model, explainer, DMatrix, data.matrix, idx,
type = "binary", threshold = 1e-04, limits = c(NA, NA))
|
xgb.model |
A trained xgboost model |
explainer |
The output from the buildExplainer function, for this model |
DMatrix |
The DMatrix in which the row to be predicted is stored |
data.matrix |
The matrix of data from which the DMatrix was built |
idx |
The row number of the data to be explained |
type |
The objective function of the model - either "binary" (for binary:logistic) or "regression" (for reg:linear) |
threshold |
Default = 0.0001. The waterfall chart will group all variables with absolute impact less than the threshold into a variable called 'Other' |
limits |
The limits of the y axis - for binary this is on logit scale (e.g. c(-3,3) would give a scale approximately from 0.04 to 0.96) |
None
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | library(xgboost)
library(xgboostExplainer)
set.seed(123)
data(agaricus.train, package='xgboost')
X = as.matrix(agaricus.train$data)
y = agaricus.train$label
train_idx = 1:5000
train.data = X[train_idx,]
test.data = X[-train_idx,]
xgb.train.data <- xgb.DMatrix(train.data, label = y[train_idx])
xgb.test.data <- xgb.DMatrix(test.data)
param <- list(objective = "binary:logistic")
xgb.model <- xgboost(param =param, data = xgb.train.data, nrounds=3)
col_names = colnames(X)
pred.train = predict(xgb.model,X)
nodes.train = predict(xgb.model,X,predleaf =TRUE)
trees = xgb.model.dt.tree(col_names, model = xgb.model)
#### The XGBoost Explainer
explainer = buildExplainer(xgb.model,xgb.train.data, type="binary", base_score = 0.5, trees = NULL)
pred.breakdown = explainPredictions(xgb.model, explainer, xgb.test.data)
showWaterfall(xgb.model, explainer, xgb.test.data, test.data, 2, type = "binary")
showWaterfall(xgb.model, explainer, xgb.test.data, test.data, 8, type = "binary")
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