Skip to content

Instantly share code, notes, and snippets.

@revodavid

revodavid/caretoutput.txt

Created December 2, 2015 22:01
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save revodavid/64703d753ad88adfb6e5 to your computer and use it in GitHub Desktop.
Save revodavid/64703d753ad88adfb6e5 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
caretoutput.txt
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
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment