Leveraging Learning to Automatically Manage Algorithms

Provides functionality to train and evaluate algorithm selection models for portfolios.



  • fix bug that caused errors in some pairwise classification cases


  • fix bug that caused errors with learners that supports weights when only a single class label is present


  • stricter argument checking: the number of folds to partition in must be an integer
  • the functions that generate partitions into train/test now overwrite any existing partitions
  • automatic tuning of models is now supported through the tuneModel function
  • mlr 2.5 compatiblity
  • classification functions can now use learners that predict probabilities
  • various small bug and reliability fixes


  • models computed during cross-validation can be saved by passing save.models to the model builders
  • various performance improvements, especially in the score computing functions
  • introduce functions for result analysis: perfScatterPlot, predTable
  • allow to create train/test splits with bootstrapping
  • stratification for the train/test split generation functions is now turned off by default
  • feature selection functionality has been retired
  • some of the internal APIs have changed -- your code may break if you rely on these


  • take success (if present) into account when determining best algorithm: if nothing was successful on an instance, set to NA -- this means that vbs may return NA as well
  • fix bugs wrt cost calculations
  • fix stupid bug that caused the incorrect best algorithm to be determined in some cases
  • some addtional small bug fixes


  • allow vbs/singleBest to operate on test splits to simplify the interface
  • corrected the implementation of contributions() to handle minimisation and maximisation of performance values correctly


  • add regressionPairs model, which predicts the performance difference for each pair of algorithms and makes decisions based on that
  • use mlr for machine learning algorithms
  • use original problem features along with predictions in stacked learners

Reference manual

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0.10.1 by Lars Kotthoff, 10 months ago


Browse source code at https://github.com/cran/llama

Authors: Lars Kotthoff [aut,cre] , Bernd Bischl [aut] , Barry Hurley [ctb] , Talal Rahwan [ctb] , Damir Pulatov [ctb]

Documentation:   PDF Manual  

BSD_3_clause + file LICENSE license

Imports rJava, parallelMap, ggplot2, checkmate, BBmisc, plyr, data.table

Depends on mlr

Suggests testthat, ParamHelpers

Imported by aslib.

See at CRAN