weightedMeanLearner {fuseMLR} | R Documentation |
The weighted mean meta-learner
Description
Modality-specific learner are assessed and weighted based on their predictions. This function is intended to be (internally) used as meta-learner in fuseMLR.
Usage
weightedMeanLearner(x, y, weighted = TRUE, perf = NULL, na_rm = FALSE)
Arguments
x |
data.frame
Modality-specific predictions. Each column of the data.frame content the predictions
a specific learner.
|
y |
vector
True target values. If classification, either binary or two level factor variable.
|
weighted |
boolean
If TRUE, a weighted sum is computed. As default, weights are estimated based on Brier Score
for classification setting and mean squared error for regression. Otherwise, use argument
perf below to specify the function to use estimate learner performance.
|
perf |
function
Function to compute layer-specific performance of learners. If NULL, the Brier Score (classification) or a mean squared error (regression) is used by default as performance measure.
Otherwise, the performance function must accept two parameters: observed (observed values) and predicted (predicted values).
|
na_rm |
boolean
Should missing values be removed when computing the weights?
|
Value
Object of class weightedMeanLearner
with the vector of estimated weights pro layer.
Examples
set.seed(20240624L)
x = data.frame(x1 = runif(n = 50L, min = 0, max = 1),
x2 = runif(n = 50L, min = 0, max = 1))
y = sample(x = 0L:1L, size = 50L, replace = TRUE)
my_model = weightedMeanLearner(x = x, y = y)
[Package
fuseMLR version 0.0.1
Index]