default_metalearner: Automatically Defined Metalearner
In tlverse/sl3: Pipelines for Machine Learning and Super Learning
View source: R/default_metalearner.R
default_metalearner R Documentation
Automatically Defined Metalearner
Description
A sensible metalearner is chosen based on the outcome type.
Usage
default_metalearner(outcome_type)
Arguments
outcome_type
a Variable_Type object
Details
For binary and continuous outcome types, the default metalearner is
non-negative least squares (NNLS) regression (Lrnr_nnls), and
for others the metalearner is Lrnr_solnp with an appropriate
loss and combination function, shown in the table below.
Outcome Type Combination Function Loss Function
categorical metalearner_linear_multinomial loss_loglik_multinomial
multivariate metalearner_linear_multivariate loss_squared_error_multivariate
tlverse/sl3 documentation built on Nov. 18, 2024, 12:46 a.m.
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View source: R/default_metalearner.R
| default_metalearner | R Documentation |
Automatically Defined Metalearner
Description
A sensible metalearner is chosen based on the outcome type.
Usage
default_metalearner(outcome_type)
Arguments
outcome_type |
a Variable_Type object |
Details
For binary and continuous outcome types, the default metalearner is
non-negative least squares (NNLS) regression (Lrnr_nnls), and
for others the metalearner is Lrnr_solnp with an appropriate
loss and combination function, shown in the table below.
| Outcome Type | Combination Function | Loss Function |
| categorical | metalearner_linear_multinomial | loss_loglik_multinomial |
| multivariate | metalearner_linear_multivariate | loss_squared_error_multivariate |
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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