# Leveraging Learning to Automatically Manage Algorithms

## 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