metalearner_discrete: Discrete meta learner
In targeted: Targeted Inference
metalearner_discrete R Documentation
Discrete meta learner
Description
Implements the discrete super learner: the candidate learner
with the lowest risk (computed via the model.score argument of
superlearner) is given weight one and all other learners weight zero.
Usage
metalearner_discrete(y, pred, model.score, ...)
Arguments
y
(numeric) Response vector.
pred
(matrix) Matrix of cross-validated predictions with one column
per candidate learner.
model.score
(function) Method for scoring the predictions of each base
learner.
...
Additional arguments (currently ignored).
Value
(numeric) Vector of ensemble weights, one element per column of
pred.
See Also
superlearner learner_sl
targeted documentation built on July 15, 2026, 9:06 a.m.
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| metalearner_discrete | R Documentation |
Discrete meta learner
Description
Implements the discrete super learner: the candidate learner
with the lowest risk (computed via the model.score argument of
superlearner) is given weight one and all other learners weight zero.
Usage
metalearner_discrete(y, pred, model.score, ...)
Arguments
y |
(numeric) Response vector. |
pred |
(matrix) Matrix of cross-validated predictions with one column per candidate learner. |
model.score |
(function) Method for scoring the predictions of each base learner. |
... |
Additional arguments (currently ignored). |
Value
(numeric) Vector of ensemble weights, one element per column of
pred.
See Also
superlearner learner_sl
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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