Some LLAMA benchmark results

LLAMA results

All results were produced by using the cross-validation splits in the repository with 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	0.843	8187.518	0.000
baseline	singleBest	0.670	16844.813	857.295
baseline	singleBestByPar	0.717	14605.904	718.386
baseline	singleBestBySuccesses	0.717	14605.904	718.386
classif	meta/AdaBoostM1	0.717	14713.518	685.975
classif	bayes/BayesNet	0.717	14713.518	685.975
classif	lazy/IBk	0.730	13975.956	548.414
classif	rules/OneR	0.707	15195.778	718.235
classif	trees/RandomTree	0.703	15288.122	660.579
classif	trees/J48	0.717	14617.680	590.137
classif	rules/JRip	0.717	14659.980	632.438
classif	classif.ctree	0.710	15030.375	702.832
classif	classif.ksvm	0.717	14666.165	638.623
classif	classif.naiveBayes	0.683	16293.808	766.265
classif	classif.randomForest	0.740	13513.452	535.909
classif	classif.rpart	0.713	14870.554	693.011
regr	regr.lm	0.733	13818.060	540.517
regr	regr.rpart	0.697	15610.789	683.247
regr	regr.randomForest	0.750	12941.271	413.728
regr	regr.earth	0.707	15211.937	734.395
cluster	EM	0.717	14713.518	685.975
cluster	FarthestFirst	0.713	14880.059	702.516
cluster	SimpleKMeans	0.717	14713.518	685.975

The following default feature steps were used for model building:

Pre, Basic, KLB, CG, DIAMETER, cl, sp, ls_saps, ls_gsat, lobjois

Number of presolved instances: 10

The cost for using the feature steps (adapted for presolving) is: 4.754652 × 10⁴ or on average: 158.4884

The feature steps correspond to the following 115 / 115 features:

nvarsOrig, nclausesOrig, nvars, nclauses, reducedVars,
reducedClauses, vars_clauses_ratio, POSNEG_RATIO_CLAUSE_mean, POSNEG_RATIO_CLAUSE_coeff_variation, POSNEG_RATIO_CLAUSE_min,
POSNEG_RATIO_CLAUSE_max, POSNEG_RATIO_CLAUSE_entropy, VCG_CLAUSE_mean, VCG_CLAUSE_coeff_variation, VCG_CLAUSE_min,
VCG_CLAUSE_max, VCG_CLAUSE_entropy, UNARY, BINARYp, TRINARYp,
VCG_VAR_mean, VCG_VAR_coeff_variation, VCG_VAR_min, VCG_VAR_max, VCG_VAR_entropy,
POSNEG_RATIO_VAR_mean, POSNEG_RATIO_VAR_stdev, POSNEG_RATIO_VAR_min, POSNEG_RATIO_VAR_max, POSNEG_RATIO_VAR_entropy,
HORNY_VAR_mean, HORNY_VAR_coeff_variation, HORNY_VAR_min, HORNY_VAR_max, HORNY_VAR_entropy,
horn_clauses_fraction, VG_mean, VG_coeff_variation, VG_min, VG_max,
CG_mean, CG_coeff_variation, CG_min, CG_max, CG_entropy,
cluster_coeff_mean, cluster_coeff_coeff_variation, cluster_coeff_min, cluster_coeff_max, cluster_coeff_entropy,
DIAMETER_mean, DIAMETER_coeff_variation, DIAMETER_min, DIAMETER_max, DIAMETER_entropy,
cl_num_mean, cl_num_coeff_variation, cl_num_min, cl_num_max, cl_num_q90,
cl_num_q10, cl_num_q75, cl_num_q25, cl_num_q50, cl_size_mean,
cl_size_coeff_variation, cl_size_min, cl_size_max, cl_size_q90, cl_size_q10,
cl_size_q75, cl_size_q25, cl_size_q50, SP_bias_mean, SP_bias_coeff_variation,
SP_bias_min, SP_bias_max, SP_bias_q90, SP_bias_q10, SP_bias_q75,
SP_bias_q25, SP_bias_q50, SP_unconstraint_mean, SP_unconstraint_coeff_variation, SP_unconstraint_min,
SP_unconstraint_max, SP_unconstraint_q90, SP_unconstraint_q10, SP_unconstraint_q75, SP_unconstraint_q25,
SP_unconstraint_q50, saps_BestSolution_Mean, saps_BestSolution_CoeffVariance, saps_FirstLocalMinStep_Mean, saps_FirstLocalMinStep_CoeffVariance,
saps_FirstLocalMinStep_Median, saps_FirstLocalMinStep_Q10, saps_FirstLocalMinStep_Q90, saps_BestAvgImprovement_Mean, saps_BestAvgImprovement_CoeffVariance,
saps_FirstLocalMinRatio_Mean, saps_FirstLocalMinRatio_CoeffVariance, gsat_BestSolution_Mean, gsat_BestSolution_CoeffVariance, gsat_FirstLocalMinStep_Mean,
gsat_FirstLocalMinStep_CoeffVariance, gsat_FirstLocalMinStep_Median, gsat_FirstLocalMinStep_Q10, gsat_FirstLocalMinStep_Q90, gsat_BestAvgImprovement_Mean,
gsat_BestAvgImprovement_CoeffVariance, gsat_FirstLocalMinRatio_Mean, gsat_FirstLocalMinRatio_CoeffVariance, lobjois_mean_depth_over_vars, lobjois_log_num_nodes_over_vars,