Resample Estimation of Model Performance
Estimation of the predictive performance of a model estimated and evaluated on training and test samples generated from an observed data set.
resample(x, ...)
## S3 method for class 'formula'
resample(x, data, model, control = MachineShop::settings("control"), ...)
## S3 method for class 'matrix'
resample(x, y, model, control = MachineShop::settings("control"), ...)
## S3 method for class 'ModelFrame'
resample(x, model, control = MachineShop::settings("control"), ...)
## S3 method for class 'recipe'
resample(x, model, control = MachineShop::settings("control"), ...)
## S3 method for class 'MLModel'
resample(x, ...)
## S3 method for class 'MLModelFunction'
resample(x, ...)x |
input specifying a relationship between model
predictor and response variables. Alternatively, a model
function or call may be given first followed by the input specification and
|
... |
arguments passed to other methods. |
data |
data frame containing observed predictors and outcomes. |
model |
model function, function name, or call; ignored and can be omitted when resampling modeled inputs. |
control |
control function, function name, or call defining the resampling method to be employed. |
y |
response variable. |
Stratified resampling is performed for the formula method according to
values of the response variable; i.e. categorical levels for factor,
continuous for numeric, and event status Surv.
User-specified stratification variables may be specified for
ModelFrames upon creation with the strata
argument in its constructor. Resampling of this class is unstratified by
default.
Variables in recipe specifications may be designated as case strata
with the role_case function. Resampling will be unstratified
otherwise.
Resamples class object.
c, metrics, performance,
plot, summary
## Requires prior installation of suggested package gbm to run ## Factor response example fo <- Species ~ . control <- CVControl() gbm_res1 <- resample(fo, iris, GBMModel(n.trees = 25), control) gbm_res2 <- resample(fo, iris, GBMModel(n.trees = 50), control) gbm_res3 <- resample(fo, iris, GBMModel(n.trees = 100), control) summary(gbm_res1) plot(gbm_res1) res <- c(GBM1 = gbm_res1, GBM2 = gbm_res2, GBM3 = gbm_res3) summary(res) plot(res)
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