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Regression Model Evaluation

Source: R/regressmdl_eval.R
regressmdl_eval.Rd

Provides calculations of measures to evaluate regression models.

Usage

regressmdl_eval(task, trained_model, splits)

Arguments

task

mlr3 regression task object

trained_model

mlr3 trained learner (model) object

splits

mlr3 object defining data splits for train and test sets

Value

Data frame containing regression evaluation measures

References

Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Au Q, Casalicchio G, Kotthoff L, Bischl B. mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software. 2019 Dec 11;4(44):1903.

See also

eCM_plot()

Examples

library("explainer")
seed <- 246
set.seed(seed)
# Load necessary packages
if (!requireNamespace("mlbench", quietly = TRUE)) stop("mlbench not installed.")
if (!requireNamespace("mlr3learners", quietly = TRUE)) stop("mlr3learners not installed.")
if (!requireNamespace("ranger", quietly = TRUE)) stop("ranger not installed.")
# Load BreastCancer dataset
utils::data("BreastCancer", package = "mlbench")
mydata <- BreastCancer[, -1]
mydata <- na.omit(mydata)
sex <- sample(
  c("Male", "Female"),
  size = nrow(mydata),
  replace = TRUE
)
mydata$age <- sample(
  seq(18, 60),
  size = nrow(mydata),
  replace = TRUE
)
mydata$sex <- factor(
  sex,
  levels = c("Male", "Female"),
  labels = c(1, 0)
)
mydata$Class <- NULL
mydata$Cl.thickness <- as.numeric(mydata$Cl.thickness)
target_col <- "Cl.thickness"
maintask <- mlr3::TaskRegr$new(
  id = "my_regression_task",
  backend = mydata,
  target = target_col
)
splits <- mlr3::partition(maintask)
mylrn <- mlr3::lrn(
  "regr.ranger",
  predict_type = "response"
)
mylrn$train(maintask, splits$train)
regressmdl_eval_results <- regressmdl_eval(
  task = maintask,
  trained_model = mylrn,
  splits = splits
)