mlexperiments (version 0.0.5)

performance: performance

Description

Calculate performance measures from the predictions results.

Usage

performance(object, prediction_results, y_ground_truth, type = NULL, ...)

Value

The function returns a data.table with the computed performance metric of each fold.

Arguments

object

An R6 object of class "MLCrossValidation" for which the performance should be computed.

prediction_results

An object of class "mlexPredictions" (the output of the function predictions()).

y_ground_truth

A vector with the ground truth of the predicted data.

type

A character to select a pre-defined set of metrics for "binary" and "regression" tasks. If not specified (default: NULL), the metrics that were specified during fitting the object are used.

...

A list. Further arguments required to compute the performance metrics.

Details

The performance metric has to be specified in the object that is used to carry out the experiment, i.e., MLCrossValidation or MLNestedCV. Please note that the option return_models = TRUE must be set in the experiment class in order to be able to compute the predictions, which are required to conduct the calculation of the performance.

Examples

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 <- list(
  auc = metric("auc"), sensitivity = metric("sensitivity"),
  specificity = metric("specificity")
)
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"
)

# performance
mlexperiments::performance(
  object = glm_optimization,
  prediction_results = preds,
  y_ground_truth = dataset[, 7],
  positive = "1"
)

# performance - binary
mlexperiments::performance(
  object = glm_optimization,
  prediction_results = preds,
  y_ground_truth = dataset[, 7],
  type = "binary",
  positive = "1"
)