12.6 GLM

# create spatial classification task
task = mlr3spatiotempcv::TaskClassifST$new(
  id = "ecuador_lsl",
  backend = mlr3::as_data_backend(lsl), # expects response and predictor vars
  target = "lslpts", 
  positive = "TRUE",
  coordinate_names = c("x", "y"),
  coords_as_features = FALSE,
  crs = "EPSG:32717"
  )

# plot response against each predictor
mlr3viz::autoplot(task, type = "duo")

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

# plot all variables against each other (slightly long computation time)
mlr3viz::autoplot(task, type = "pairs")

pairs plot

• The mlr3extralearners package contains more information (but mlr3extralearners was not currently available for R 4.3.0)

Class	Name	Short name	Package
classif.adaboostm1	ada Boosting M1	adaboostm1	RWeka
classif.binomial	Binomial Regression	binomial	stats
classif.featureless	Featureless classifier	featureless	mlr
classif.fnn	Fast k-Nearest Neighbour	fnn	FNN
classif.gausspr	Gaussian Processes	gausspr	kernlab
classif.IBk	k-Nearest Neighbours	ibk	RWeka

learner = mlr3::lrn("classif.log_reg", predict_type = "prob")
# learner$help()