Here is the feature subset we used in our CP06 paper "Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms". It is actually only 43 features, not 46 as stated in the paper, sorry about that. We'll correct it in the final version of the paper. As a preprocessing step in the machine learning, constant features were removed (features for which the maximum and mimimum value differed by no more than 10^(-10).) Software to quickly extract these features from any given SAT instances will be available soon. nvars nclauses vars_clauses_ratio VCG_VAR_min VCG_VAR_max VCG_VAR_entropy VCG_CLAUSE_mean VCG_CLAUSE_coeff_variation VCG_CLAUSE_max VCG_CLAUSE_entropy POSNEG_RATIO_CLAUSE_mean POSNEG_RATIO_VAR_stdev POSNEG_RATIO_VAR_max BINARY_PLUS TRINARY_PLUS HORNY_VAR_coeff_variation HORNY_VAR_min HORNY_VAR_max HORNY_VAR_entropy horn_clauses_fraction VG_mean VG_coeff_variation VG_min vars_reduced_depth_4 vars_reduced_depth_16 vars_reduced_depth_64 saps_BestSolution_Mean saps_BestSolution_CoeffVariance saps_BestStep_Mean saps_BestStep_CoeffVariance saps_AvgImproveToBest_Mean saps_AvgImproveToBest_CoeffVariance saps_FirstLMRatio_Mean saps_FirstLMRatio_CoeffVariance saps_BestCV_Mean gsat_BestSolution_Mean gsat_BestSolution_CoeffVariance gsat_BestStep_Mean gsat_BestStep_CoeffVariance gsat_AvgImproveToBest_CoeffVariance gsat_BestCV_Mean lobjois_mean_depth_over_vars lobjois_log_num_nodes_over_vars