Description Usage Arguments Details Value Author(s) Examples
Performs prediction of response lm
, glm
,
ranger
, mgcv::gam
and
xgboost::xgb.train
with binary or continuous
response. See details for more information.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  predict_model(x, newdata)
## Default S3 method:
predict_model(x, newdata)
## S3 method for class 'lm'
predict_model(x, newdata)
## S3 method for class 'glm'
predict_model(x, newdata)
## S3 method for class 'ranger'
predict_model(x, newdata)
## S3 method for class 'xgb.Booster'
predict_model(x, newdata)
## S3 method for class 'gam'
predict_model(x, newdata)

x 
Model object for the model to be explained. 
newdata 
A data frame (or matrix) in which to look for variables with which to predict. 
The following models are currently supported:
stats::lm
stats::glm
ranger::ranger
mgcv::gam
xgboost::xgb.train
The returned object p
always satisfies the following properties:
is.atomic(p)
equals TRUE
is.double(p)
equals TRUE
If you have a binary classification model we'll always return the probability prediction for a single class.
For more details on how to explain other types of models (i.e. custom models), see the Advanced usage section
of the vignette:
From R: vignette("understanding_shapr", package = "shapr")
Web: https://norskregnesentral.github.io/shapr/articles/understanding_shapr.html#explaincustommodels
Numeric
Martin Jullum
1 2 3 4 5 6 7 8 9 10 11 12  if (requireNamespace("MASS", quietly = TRUE)) {
# Load example data
data("Boston", package = "MASS")
# Split data into test and training data
x_train < head(Boston, 3)
x_test < tail(Boston, 3)
# Fit a linear model
model < lm(medv ~ lstat + rm + dis + indus, data = x_train)
# Predicting for a model with a standardized format
predict_model(x = model, newdata = x_test)
}

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