Notei que o uso de métodos de fórmula e não-fórmula em acento circunflexo durante o treinamento produz resultados diferentes. Além disso, o tempo necessário para o método de fórmula é quase 10x o tempo gasto para o método de não fórmula. Isso é esperado?

> z <- data.table(c1=sample(1:1000,1000, replace=T), c2=as.factor(sample(LETTERS, 1000, replace=T)))

# SYSTEM TIME WITH FORMULA METHOD
# -------------------------------

> system.time(r <- train(c1 ~ ., z, method="rf", importance=T))
   user  system elapsed
376.233   9.241  18.190

> r
1000 samples
   1 predictors

No pre-processing
Resampling: Bootstrap (25 reps)

Summary of sample sizes: 1000, 1000, 1000, 1000, 1000, 1000, ...

Resampling results across tuning parameters:

  mtry  RMSE  Rsquared  RMSE SD  Rsquared SD
  2     295   0.00114   4.94     0.00154
  13    300   0.00113   5.15     0.00151
  25    300   0.00111   5.16     0.00146

RMSE was used to select the optimal model using  the smallest value.
The final value used for the model was mtry = 2.


# SYSTEM TIME WITH NON-FORMULA METHOD
# -------------------------------

> system.time(r <- train(z[,2,with=F], z$c1, method="rf", importance=T))
       user  system elapsed
     34.984   2.977   2.708
    Warning message:
    In randomForest.default(trainX, trainY, mtry = tuneValue$.mtry,  :
  invalid mtry: reset to within valid range
> r
1000 samples
   1 predictors

No pre-processing
Resampling: Bootstrap (25 reps)

Summary of sample sizes: 1000, 1000, 1000, 1000, 1000, 1000, ...

Resampling results

  RMSE  Rsquared  RMSE SD  Rsquared SD
  297   0.00152   6.67     0.00197

Tuning parameter 'mtry' was held constant at a value of 2