View source: R/cv_predictions.R
predictions | R Documentation |
Apply an R6 object of class "MLCrossValidation"
to new data
to compute predictions.
predictions(object, newdata, na.rm = FALSE, ncores = -1L, ...)
object |
An R6 object of class |
newdata |
The new data for which predictions should be made using
the |
na.rm |
A logical. If missings should be removed before computing the
mean and standard deviation of the performance across different folds for
each observation in |
ncores |
An integer to specify the number of cores used for
parallelization (default: |
... |
A list. Further arguments required to compute the predictions. |
The function returns a data.table of class "mlexPredictions"
with
one row for each observation in newdata
and the columns containing
the predictions for each fold, along with the mean and standard deviation
across all folds.
dataset <- do.call(
cbind,
c(sapply(paste0("col", 1:6), function(x) {
rnorm(n = 500)
},
USE.NAMES = TRUE,
simplify = FALSE
),
list(target = sample(0:1, 500, TRUE))
))
fold_list <- splitTools::create_folds(
y = dataset[, 7],
k = 3,
type = "stratified",
seed = 123
)
glm_optimization <- mlexperiments::MLCrossValidation$new(
learner = LearnerGlm$new(),
fold_list = fold_list,
seed = 123
)
glm_optimization$learner_args <- list(family = binomial(link = "logit"))
glm_optimization$predict_args <- list(type = "response")
glm_optimization$performance_metric_args <- list(positive = "1")
glm_optimization$performance_metric <- metric("auc")
glm_optimization$return_models <- TRUE
# set data
glm_optimization$set_data(
x = data.matrix(dataset[, -7]),
y = dataset[, 7]
)
cv_results <- glm_optimization$execute()
# predictions
preds <- mlexperiments::predictions(
object = glm_optimization,
newdata = data.matrix(dataset[, -7]),
na.rm = FALSE,
ncores = 2L,
type = "response"
)
head(preds)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.