Leveraging Learning to Automatically Manage Algorithms

analysis | Analysis functions |

bsFolds | Bootstrapping folds |

classify | Classification model |

classifyPairs | Classification model for pairs of algorithms |

cluster | Cluster model |

cvFolds | Cross-validation folds |

helpers | Helpers |

imputeCensored | Impute censored values |

input | Read data |

llama-package | Leveraging Learning to Automatically Manage Algorithms |

misc | Convenience functions |

misclassificationPenalties | Misclassification penalty |

normalize | Normalize features |

parscores | Penalized average runtime score |

plot | Plot convenience functions to visualise selectors |

regression | Regression model |

regressionPairs | Regression model for pairs of algorithms |

satsolvers | Example data for Leveraging Learning to Automatically Manage... |

successes | Success |

trainTest | Train / test split |

tune | Tune the hyperparameters of the machine learning algorithm... |

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