ModelSpecification  R Documentation 
Specification of a relationship between response and predictor variables and a model to define a relationship between them.
ModelSpecification(...) ## Default S3 method: ModelSpecification( input, model = NULL, control = MachineShop::settings("control"), metrics = NULL, cutoff = MachineShop::settings("cutoff"), stat = MachineShop::settings("stat.TrainingParams"), ... ) ## S3 method for class 'formula' ModelSpecification(formula, data, model, ...) ## S3 method for class 'matrix' ModelSpecification(x, y, model, ...) ## S3 method for class 'ModelFrame' ModelSpecification(input, model = NULL, ...) ## S3 method for class 'recipe' ModelSpecification(input, model = NULL, ...)
... 
arguments passed from the generic function to its methods. The
first argument of each 
input 
input object defining and containing the model predictor and response variables. 
model 
model function, function name, or object; or another object that can be coerced to a model. 
control 
control function, function name, or object
defining the resampling method to be employed. If

metrics 
metric function, function name, or vector of these with which to calculate performance. If not specified, default metrics defined in the performance functions are used. Model selection is based on the first calculated metric. 
cutoff 
argument passed to the 
stat 
function or character string naming a function to compute a summary statistic on resampled metric values for model tuning. 
formula, data 
formula defining the model predictor and response variables and a data frame containing them. 
x, y 
matrix and object containing predictor and response variables. 
ModelSpecification
class object.
fit
, resample
,
set_monitor
, set_optim
## Requires prior installation of suggested package gbm to run modelspec < ModelSpecification( sale_amount ~ ., data = ICHomes, model = GBMModel ) fit(modelspec)
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