Determine SuperLearner Weights with Nonnegative Least Squares — determine_super_learner_weights

This function accepts a dataframe that is structured to have one column Y and other columns with unique names corresponding to different model predictions for Y, and it will use nonnegative least squares to determine the weights to use for a SuperLearner.

Usage

determine_super_learner_weights_nnls(data, y_variable, obs_weights = NULL)

Arguments

data: A data frame consisting of an outcome (y_variable) and other columns corresponding to predictions from candidate learners.
y_variable: The string name of the outcome column in data.
obs_weights: A vector of weights for each observation that dictate how prediction should be more targeted to higher weighted observations.

Value

A vector of weights to be used for each of the learners.

Examples

# suppose that we have a data.frame of predictions from different candidate
# learners:
prediction_data <- data.frame(
  lm = lnr_lm(mtcars, mpg ~ hp)(mtcars),
  rf = lnr_rf(mtcars, mpg ~ hp)(mtcars),
  rf2 = lnr_rf(mtcars, mpg ~ hp, ntree = 20)(mtcars),
  earth = lnr_earth(mtcars, mpg ~ hp)(mtcars)
)
# make sure it includes the outcome y_variable
prediction_data$mpg <- mtcars$mpg

# we can use determine_super_learner_weights() fn to apply the non-negative least
# squares algorithm to produce weights for averaging the learners
determine_super_learner_weights_nnls(
  data = prediction_data,
  y_variable = "mpg"
)
#> [1] 0.000000 0.253233 0.746767 0.000000