Created
December 2, 2015 22:01
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rf_ga3 | |
1010 samples | |
58 predictors | |
2 classes: 'PS', 'WS' | |
Maximum generations: 100 | |
Population per generation: 20 | |
Crossover probability: 0.8 | |
Mutation probability: 0.1 | |
Elitism: 0 | |
Internal performance values: Accuracy, Kappa | |
Subset selection driven to maximize internal Accuracy | |
External performance values: Accuracy, Kappa | |
Best iteration chose by maximizing external Accuracy | |
External resampling method: Cross-Validated (10 fold) | |
During resampling: | |
* the top 5 selected variables (out of a possible 58): | |
DiffIntenDensityCh4 (100%), EntropyIntenCh1 (100%), EqEllipseProlateVolCh1 (100%), EqSphereAreaCh1 (100%), FiberWidthCh1 (100%) | |
* on average, 39.3 variables were selected (min = 25, max = 52) | |
In the final search using the entire training set: | |
* 44 features selected at iteration 9 including: | |
AvgIntenCh1, AvgIntenCh4, ConvexHullAreaRatioCh1, ConvexHullPerimRatioCh1, EntropyIntenCh3 ... | |
* external performance at this iteration is | |
Accuracy Kappa | |
0.8406 0.6513 | |
plot(rf_ga3) # Plot mean fitness (AUC) by generation |
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