`10`

folds and `1`

repetitions.The best values within a type (i.e., baseline (except for vbs), classif, regr and cluster) and performance measure (i.e., Percentage solved, PAR10, MCP) are colored green. Furthermore, the three best values over all groups within a performance measure are colored pink, the absolute best one is red.

The performance is measured in three different ways.

**Percentage solved**records the percentage of problem instances in the data set for which the selector selected an algorithm that was able to solve it with runstatus "ok" and the algorithm time plus the feature computation time was at most the timeout.- The
**penalized average runtime score (PAR10)**measures the time required to run on all problem instances. If an instance was solved within the timeout by the algorithm the selector chose, the actual runtime is taken. If a timeout occurred, the timeout value was multiplied by 10. - The
**misclassification penalty (mcp)**measures the additional time required to run on all problems if sub-optimal algorithms were used. That is, if an algorithm is run on a problem instance that is not the best, a performance loss is incurred. There are no additional penalties or factors for timeouts. The virtual best solver always has a misclassification penalty of zero.

algo | model | succ | par10 | mcp |
---|---|---|---|---|

baseline | vbs | 0.770 | 8337.099 | 0.000 |

baseline | singleBest | 0.396 | 21884.319 | 1420.904 |

baseline | singleBestByPar | 0.577 | 15330.171 | 716.756 |

baseline | singleBestBySuccesses | 0.577 | 15330.171 | 716.756 |

classif | meta/AdaBoostM1 | 0.577 | 15330.171 | 716.756 |

classif | lazy/IBk | 0.692 | 11159.665 | 288.355 |

classif | rules/OneR | 0.651 | 12657.656 | 436.346 |

classif | trees/RandomTree | 0.716 | 10305.316 | 215.585 |

classif | trees/J48 | 0.707 | 10637.255 | 239.629 |

classif | rules/JRip | 0.700 | 10878.806 | 268.023 |

classif | classif.ctree | 0.691 | 11224.438 | 305.760 |

classif | classif.ksvm | 0.691 | 11221.558 | 302.879 |

classif | classif.naiveBayes | 0.491 | 18410.085 | 1025.617 |

classif | classif.randomForest | 0.730 | 9800.574 | 160.843 |

classif | classif.rpart | 0.689 | 11288.085 | 298.354 |

regr | regr.lm | 0.714 | 10388.712 | 227.928 |

regr | regr.rpart | 0.714 | 10405.535 | 244.751 |

regr | regr.randomForest | 0.749 | 9152.113 | 104.487 |

regr | regr.earth | 0.721 | 10132.304 | 208.362 |

cluster | EM | 0.641 | 13022.871 | 493.666 |

cluster | FarthestFirst | 0.577 | 15330.171 | 716.756 |

cluster | SimpleKMeans | 0.577 | 15330.171 | 716.756 |

The following default feature steps were used for model building:

`all_feats`

Number of presolved instances: `0`

The cost for using the feature steps (adapted for presolving) is: `0`

or on average: `NA`

The feature steps correspond to the following `46`

/ `46`

features:

EXIST_VARS, FORALL_VARS, TOTAL_VARS, CLAUSES, LITERALS,

EXIST_SET, FORALL_SET, TOTAL_SET, UNARY_CLAUSES, BINARY_CLAUSES,

TERNARY_MORE_CLAUSES, POS_HORN, NEG_HORN, EXIST_LIT_PER_CLAUSE, FORALL_LIT_PER_CLAUSE,

EXIST_VARS_PER_SET, FORALL_POS_LITS_PER_CLAUSE, FORALL_NEG_LITS_PER_CLAUSE, OCCS_POS_NO_PER_VAR, OCCS_FORALL_NO_PER_VAR,

OCCS_FORALL_POS_NO_PER_VAR, W_OCCS_POS_NO_PER_VAR, W_OCCS_FORALL_NO_PER_VAR, W_OCCS_FORALL_POS_NO_PER_VAR, W_PRODUCTS,

LITN_LIT, LITEP_LIT, LITEN_LITE, LITEN_LITN, LITFN_LIT,

LITFP_LITFN, OCCP_OCCN, OCCE_OCC, OCCEN_OCC, OCCFP_OCCF,

OCCEN_OCCE, OCCEN_OCCN, OCCFP_OCCFN, TERMORE_CLAUSE, NEG_HORN_CLAUSE,

WOCCN_WOCC, WOCCEP_WOCC, WOCCFN_WOCC, WOCCEP_WOCCE, WOCCEP_WOCCP,

WOCCFN_WOCCN,