Leveraging Learning to Automatically Manage Algorithms

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Documentation for package ‘llama’ version 0.10.1

• DESCRIPTION file.
• Package NEWS.

Help Pages

llama-package	Leveraging Learning to Automatically Manage Algorithms
bsFolds	Bootstrapping folds
classify	Classification model
classifyPairs	Classification model for pairs of algorithms
cluster	Cluster model
contributions	Analysis functions
cvFolds	Cross-validation folds
imputeCensored	Impute censored values
input	Read data
llama	Leveraging Learning to Automatically Manage Algorithms
makeRLearner.classif.constant	Helpers
misclassificationPenalties	Misclassification penalty
normalize	Normalize features
parscores	Penalized average runtime score
perfScatterPlot	Plot convenience functions to visualise selectors
predictLearner.classif.constant	Helpers
predTable	Convenience functions
print.llama.data	Helpers
print.llama.model	Helpers
regression	Regression model
regressionPairs	Regression model for pairs of algorithms
satsolvers	Example data for Leveraging Learning to Automatically Manage Algorithms
singleBest	Convenience functions
singleBestByCount	Convenience functions
singleBestByPar	Convenience functions
singleBestBySuccesses	Convenience functions
successes	Success
trainLearner.classif.constant	Helpers
trainTest	Train / test split
tuneModel	Tune the hyperparameters of the machine learning algorithm underlying a model
vbs	Convenience functions