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Binary Learners in {nadir}

Source: R/binary_learners.R
binary_learners.Rd

  • lnr_nnet

  • lnr_rf_binary

  • lnr_logistic

Value

Every learner function shares the same contract: called with (data, formula, ...) it fits the underlying model and returns a prediction closure which is a function of newdata returning a numeric vector of predictions (predicted probabilities of the second factor level for binary learners; predicted densities for density learners; a matrix of class probabilities for multiclass learners).

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()

See also

density_learners learners

Examples

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"
)
#> 
#> Super Learner (nadir_sl_model)
#>   outcome:      am (binary)
#>   observations: 32   CV folds: 5
#>   ensemble weights:
#>     logistic1  0.671
#>     rf_binary  0.329
#>     logistic2  0.000
#>   captured conditions (see $errors_from_*, $warnings_from_*):
#>     [warning] logistic1 @ cv-training: glm.fit: fitted probabilities numerically 0 or 1 occurred (x5)
#>     [warning] logistic2 @ cv-training: glm.fit: fitted probabilities numerically 0 or 1 occurred (x5)
#>     [warning] logistic1 @ full-data fit: glm.fit: algorithm did not converge
#>     [warning] logistic1 @ full-data fit: glm.fit: fitted probabilities numerically 0 or 1 occurred
#>     ... and 2 more unique conditions not shown
#> 
#> Methods: predict(x, newdata), plot(x), summary(x), coef(x), fitted(x), ...