predict.simple_ensemble: Predict for a simple ensemble set

Description

Predict for a new dataset by using a simple ensemble. Predictions from individual models (i.e. workflows) are combined according to fun: if a weighted function is used (weighted_mean or weighted_median), weights are based on the metric used to tune models in the ensemble (see simple_ensemble).

Usage

# S3 method for simple_ensemble
predict(
  object,
  new_data = NULL,
  type = "prob",
  fun = "mean",
  metric_thresh = NULL,
  class_thresh = NULL,
  members = FALSE,
  ...
)

Value

a tibble of predictions

Arguments

object: an simple_ensemble object
new_data: a data frame in which to look for variables with which to predict. If NULL, the predictors from the first workflow in the ensemble are used; note that this only makes sense if all workflows have the same predictors.
type: the type of prediction, "prob" or "class".
fun: string defining the aggregating function. It can take values mean, median, weighted_mean, weighted_median and none. It is possible to combine multiple functions, except for "none". If it is set to "none", only the individual member predictions are returned (this automatically sets member to TRUE). Weights are based on the metric used to tune models in the ensemble (see simple_ensemble); the weights are proportional to the metric values (so, a model with a metric that is 10% higher than another model will have a weight that is 10% higher).
metric_thresh: a vector of length 2 giving a metric and its threshold, which will be used to prune which models in the ensemble will be used for the prediction. The 'metrics' need to have been computed when the workflow was tuned. Examples are c("accuracy",0.8) or c("boyce_cont",0.7)
class_thresh: probability threshold used to convert probabilities into classes. It can be a number (between 0 and 1), or a character metric (currently "tss_max", "kap_max", or "sensitivity"). For sensitivity, an additional target value is passed along as a second element of a vector, e.g. c("sensitivity",0.8).
members: boolean defining whether individual predictions for each member should be added to the ensemble prediction. The columns for individual members have the name of the workflow a a prefix, separated by "." from the usual column names of the predictions.
...: not used in this method.