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a guest Jun 13th, 2018 57 Never
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  1. Random Forest
  2.  
  3.   44 samples
  4. 1000 predictors
  5.  
  6. No pre-processing
  7. Resampling: Cross-Validated (10 fold)
  8. Summary of sample sizes: 40, 39, 40, 40, 40, 40, ...
  9. Resampling results across tuning parameters:
  10.  
  11.   mtry  RMSE         Rsquared   MAE        
  12.    2    0.001762244  0.8374687  0.001510297                                                              
  13.    3    0.001763794  0.8220995  0.001507957
  14.    4    0.001784599  0.8018954  0.001523252                                                              
  15.    5    0.001785400  0.7992725  0.001528275                                                              
  16.   10    0.001813925  0.7805094  0.001548873
  17.   25    0.001862114  0.7484289  0.001588793
  18.   50    0.001892789  0.7324827  0.001614362
  19.  
  20. RMSE was used to select the optimal model using the smallest value.
  21. The final value used for the model was mtry = 2.
  22.    
  23. Random Forest                                                                                            
  24.  
  25.   44 samples                                                                                              
  26. 1000 predictors                                                                                          
  27.    2 classes: 'a', 'b'                                                                                    
  28.  
  29. No pre-processing
  30. Resampling: Cross-Validated (10 fold)
  31. Summary of sample sizes: 39, 39, 40, 40, 40, 40, ...                      
  32. Resampling results across tuning parameters:      
  33.  
  34.   mtry  Accuracy  Kappa                                                    
  35.    2    0.960     0.9090909
  36.    3    0.960     0.9090909                      
  37.    4    0.960     0.9090909
  38.    5    0.960     0.9090909                                                                      
  39.   10    0.960     0.9090909
  40.   25    0.935     0.8590909
  41.   50    0.915     0.8136364                                                                    
  42.  
  43. Accuracy was used to select the optimal model using the largest value.        
  44. The final value used for the model was mtry = 2.
  45.    
  46. # values of mtry to test
  47. tune_grid <- data.frame(mtry=c(2, 3, 4, 5, 10, 25, 50))
  48.  
  49. # 10-fold cross validation (classProb set to TRUE for classification models)
  50. train_control <- trainControl(method="cv", number=10, savePred=TRUE, classProb=FALSE)
  51.  
  52. train(response ~ ., data=training_set, method="rf", ntree=5000,
  53.                     trControl=train_control, tuneGrid=tune_grid)
  54.    
  55. > cor_mat <- cor(training_set[, -1001], method='spearman')                                              
  56. > quantile(cor_mat, probs=seq(0, 1, by=0.1))                                                              
  57.          0%         10%         20%         30%         40%         50%                                  
  58. -0.90500352 -0.32445384 -0.23044397 -0.15052854 -0.07019027  0.01451727                                  
  59.         60%         70%         80%         90%        100%                                              
  60.  0.09880545  0.17688513  0.25567301  0.35334743  1.00000000
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