Description Usage Arguments Details Value See Also Examples
Create a model for use with the MachineShop package.
1 2 3 4 5 6 7 8 9 10 11 12 13  MLModel(
name = "MLModel",
label = name,
packages = character(),
response_types = character(),
predictor_encoding = c(NA, "model.frame", "model.matrix"),
params = list(),
gridinfo = tibble::tibble(param = character(), values = list(), default = logical()),
fit = function(formula, data, weights, ...) stop("no fit function"),
predict = function(object, newdata, times, ...) stop("no predict function"),
varimp = function(object, ...) NULL,
...
)

name 
character name of the object to which the model is assigned. 
label 
optional character descriptor for the model. 
packages 
character vector of package names upon which the model
depends. Each name may be optionally followed by a comment in
parentheses specifying a version requirement. The comment should contain
a comparison operator, whitespace and a valid version number, e.g.

response_types 
character vector of response variable types to which
the model can be fit. Supported types are 
predictor_encoding 
character string indicating whether the model is
fit with predictor variables encoded as a 
params 
list of userspecified model parameters to be passed to the

gridinfo 
tibble of information for construction of tuning grids
consisting of a character column 
fit 
model fitting function whose arguments are a 
predict 
model prediction function whose arguments are the

varimp 
variable importance function whose arguments are the

... 
arguments passed from other methods. 
If supplied, the grid
function should return a list whose elements are
named after and contain values of parameters to include in a tuning grid to
be constructed automatically by the package.
Argument data
in the fit
function may be converted to a data
frame with the as.data.frame
function as needed. The function should
return the object resulting from the model fit.
Values returned by the predict
functions should be formatted according
to the response variable types below.
vector or column matrix of probabilities for the second level of binary factors or a matrix whose columns contain the probabilities for factors with more than two levels.
matrix of predicted responses.
vector or column matrix of predicted responses.
matrix whose columns contain survival probabilities at
times
if supplied or a vector of predicted survival means
otherwise.
The varimp
function should return a vector of importance values named
after the predictor variables or a matrix or data frame whose rows are named
after the predictors.
MLModel
class object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## Logistic regression model
LogisticModel < MLModel(
name = "LogisticModel",
response_types = "binary",
fit = function(formula, data, weights, ...) {
glm(formula, data = data, weights = weights, family = binomial, ...)
},
predict = function(object, newdata, ...) {
predict(object, newdata = newdata, type = "response")
},
varimp = function(object, ...) {
pchisq(coef(object)^2 / diag(vcov(object)), 1)
}
)
data(Pima.tr, package = "MASS")
res < resample(type ~ ., data = Pima.tr, model = LogisticModel)
summary(res)

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