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	0.701	3662.241	0.000
baseline	singleBest	0.348	7882.393	404.246
baseline	singleBestByPar	0.477	6338.901	254.757
baseline	singleBestBySuccesses	0.477	6338.901	254.757
classif	meta/AdaBoostM1	0.472	6408.151	249.150
classif	bayes/BayesNet	0.583	5084.882	122.752
classif	lazy/IBk	0.617	4688.221	92.192
classif	rules/OneR	0.518	5864.419	198.247
classif	trees/RandomTree	0.609	4776.721	96.207
classif	trees/J48	0.596	4928.165	106.843
classif	rules/JRip	0.561	5355.749	154.244
classif	classif.ctree	0.550	5478.330	164.179
classif	classif.ksvm	0.550	5482.914	168.763
classif	classif.naiveBayes	0.532	5684.669	173.387
classif	classif.randomForest	0.630	4528.243	73.023
classif	classif.rpart	0.563	5319.075	145.733
regr	regr.lm	0.619	4655.247	87.380
regr	regr.rpart	0.559	5360.441	144.855
regr	regr.randomForest	0.647	4329.539	57.370
regr	regr.earth	0.601	4870.717	105.718
cluster	EM	0.473	6391.859	246.939
cluster	FarthestFirst	0.472	6408.151	249.150
cluster	SimpleKMeans	0.472	6408.151	249.150

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: 5

The cost for using the feature steps (adapted for presolving) is: 3.761259 × 10⁴ or on average: 49.0385789

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,