Binary Learners in {nadir}

• lnr_nnet

• lnr_rf_binary

• lnr_logistic

• lnr_multinomial_nnet

• lnr_multinomial_vglm

Details

The important thing to know about binary learners is that they need to produce predictions that the outcome is == 1 or TRUE.

Also, for binary outcomes, we should make sure to use the determine_weights_for_binary_outcomes in our calls to super_learner() which calculates the estimated probability of the observed outcome (either 0 or 1) and then applies the negative log loss function afterwards. This can be done automatically by declaring outcome_type = 'binary' in calling super_learner()

Suppose one of these is trained on some data and the fit learner is stored. Suppose we are going to call it on newdata and newdata$class is the outcome variable being predicting.

The important thing to know about multiclass learners is that they produce predictions that the outcome class is equal to newdata$class given the covariates specified in newdata.

Similar to density estimation, we want to use determine_weights_using_neg_log_loss in our calls to super_learner(). This can be done automatically by declaring outcome_type = 'multiclass' in calling super_learner()

See also

density_learners learners

density_learners learners

Examples

if (FALSE) { # \dontrun{
  super_learner(
    data = mtcars,
    learners = list(logistic1 = lnr_logistic, logistic2 = lnr_logistic, lnr_rf_binary),
    formulas = list(
    .default = am ~ .,
    logistic2 = am ~ mpg * hp + .),
    outcome_type = 'binary',
    verbose = TRUE
    )
} # }

if (FALSE) { # \dontrun{
  super_learner(
    data = iris,
    learners = list(lnr_multinomial_vglm, lnr_multinomial_vglm, lnr_multinomial_nnet),
    formulas = list(
    .default = Species ~ .,
    multinomial_vglm2 = Species ~ Petal.Length*Petal.Width + .),
    outcome_type = 'multiclass',
    verbose = TRUE
    )
} # }