10
folds and 1
repetitions.The performance is measured in three different ways.
## Warning in minmax(perf2): no non-missing arguments to max; returning -Inf
algo | model | succ | par10 | mcp |
---|---|---|---|---|
baseline | vbs | 1.000 | 0.873 | 0.000 |
baseline | singleBest | 1.000 | 0.856 | 0.018 |
baseline | singleBestByPar | 1.000 | 0.423 | 0.450 |
baseline | singleBestBySuccesses | 1.000 | 0.647 | 0.226 |
regr | lm | 1.000 | 0.853 | 0.020 |
regr | rpart | 1.000 | 0.777 | 0.096 |
regr | randomForest | 1.000 | 0.846 | 0.028 |
The following default feature steps were used for model building:
ALL, code, AST
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 103
/ 103
instance features:
MeanAttributeEntropy, NaiveBayesAdwin.warnings, NoiseToSignalRatio, NumberOfNumericFeatures, NumberOfBinaryFeatures,
DecisionStumpKappa, Quartile3SkewnessOfNumericAtts, NumberOfMissingValues, J48.001.ErrRate, HoeffdingAdwin.changes,
PercentageOfNumericFeatures, MeanSkewnessOfNumericAtts, MinStdDevOfNumericAtts, NaiveBayesErrRate, MinMutualInformation,
MajorityClassPercentage, NumberOfSymbolicFeatures, J48.00001.ErrRate, MaxNominalAttDistinctValues, PercentageOfMissingValues,
MinKurtosisOfNumericAtts, MaxKurtosisOfNumericAtts, EquivalentNumberOfAtts, DecisionStumpErrRate, RandomTreeDepth3ErrRate,
MaxStdDevOfNumericAtts, NaiveBayesAdwin.changes, Quartile3AttributeEntropy, MeanKurtosisOfNumericAtts, MinorityClassPerentage,
J48.00001.Kappa, REPTreeDepth2ErrRate, Quartile2KurtosisOfNumericAtts, REPTreeDepth3AUC, RandomTreeDepth2ErrRate,
Quartile1AttributeEntropy, MeanMeansOfNumericAtts, MeanStdDevOfNumericAtts, REPTreeDepth1Kappa, Dimensionality,
REPTreeDepth2AUC, MinAttributeEntropy, NaiveBayesDdm.changes, MinNominalAttDistinctValues, MinorityClassSize,
Quartile2AttributeEntropy, Quartile1SkewnessOfNumericAtts, Quartile3StdDevOfNumericAtts, Quartile1MutualInformation, Quartile2SkewnessOfNumericAtts,
MajorityClassSize, REPTreeDepth3ErrRate, MaxAttributeEntropy, RandomTreeDepth2Kappa, HoeffdingDDM.changes,
Quartile3KurtosisOfNumericAtts, NaiveBayesKappa, HoeffdingDDM.warnings, Quartile2StdDevOfNumericAtts, MeanNominalAttDistinctValues,
REPTreeDepth1ErrRate, MaxMeansOfNumericAtts, NumberOfInstances, RandomTreeDepth1Kappa, J48.001.AUC,
MaxSkewnessOfNumericAtts, J48.0001.AUC, RandomTreeDepth3AUC, MeanMutualInformation, RandomTreeDepth1ErrRate,
RandomTreeDepth2AUC, MinMeansOfNumericAtts, PercentageOfBinaryFeatures, NumberOfFeatures, NaiveBayesAUC,
DefaultAccuracy, NaiveBayesDdm.warnings, Quartile3MeansOfNumericAtts, REPTreeDepth1AUC, MaxMutualInformation,
HoeffdingAdwin.warnings, RandomTreeDepth1AUC, J48.0001.Kappa, Quartile1MeansOfNumericAtts, RandomTreeDepth3Kappa,
Quartile1StdDevOfNumericAtts, REPTreeDepth2Kappa, Quartile2MeansOfNumericAtts, J48.00001.AUC, Quartile2MutualInformation,
J48.001.Kappa, PercentageOfInstancesWithMissingValues, NumberOfClasses, StdvNominalAttDistinctValues, ClassEntropy,
Quartile1KurtosisOfNumericAtts, Quartile3MutualInformation, MinSkewnessOfNumericAtts, PercentageOfSymbolicFeatures, J48.0001.ErrRate,
NumberOfInstancesWithMissingValues, REPTreeDepth3Kappa, DecisionStumpAUC
The feature steps correspond to the following 26
/ 26
algorithm features:
Lines..Average., Lines..Total., Size..Average., Size..Total., Number.of.files,
Cyclomatic..Average., Cyclomatic..Total., Max.Indent..Average., Max.Indent..Total., nb_nodes,
nb_edges, degree_min, degree_max, degree_mean, degree_variance,
degree_entropy, transitivity, clustering_min, clustering_max, clustering_mean,
clustering_variance, path_min, paths_max, path_mean, path_variance,
path_entropy
21
to 1
, resulting in a PAR10 score of 0.423
for the reduced model. Analogously, the model that was generated based on 1
of the originally 118
features resulted in a PAR10 score of 0.647
.
Below, you can find the list of the selected features and solvers:
Selected Features:
MeanAttributeEntropy
Selected Solvers:
X2893_weka.OLM