trainEnsemble: Train a stacked ensemble of learning algorithms and return...
In AleMorales/SeedSorter: Supervised classification of seeds from flow cytometry
Description
Usage
Arguments
Details
Value
Description
Train a stacked ensemble of learning algorithms and return the resulting model.
Usage
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trainEnsemble(
algorithms = c("extinction", "naiveBayes", "lda", "qda"),
task,
osw.rate = 10
)
Arguments
algorithms
Names of the algorithm to be used as a character vector.
task
A classification task as returned by createTrainingTask().
Details
The list of algorithms that can be used are the same as for trainAlgorithm().
The stacked ensemble calculates a weight for each algorithm based on a hill climbing approach.
Value
A trained model that can be used to make predictions.
AleMorales/SeedSorter documentation built on Feb. 12, 2020, 4:13 a.m.
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Description Usage Arguments Details Value
Description
Train a stacked ensemble of learning algorithms and return the resulting model.
Usage
1 2 3 4 5 | trainEnsemble(
algorithms = c("extinction", "naiveBayes", "lda", "qda"),
task,
osw.rate = 10
)
|
Arguments
algorithms |
Names of the algorithm to be used as a character vector. |
task |
A classification task as returned by |
Details
The list of algorithms that can be used are the same as for trainAlgorithm().
The stacked ensemble calculates a weight for each algorithm based on a hill climbing approach.
Value
A trained model that can be used to make predictions.
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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