Tuning number of trees hyperparameter. Trains random forests with a range of number of trees so the optimal number can be identified (using the resulting plot) with cross validation

selectrfnumtrees(
  data,
  maxnum = 100,
  title = "",
  showplots = T,
  output_prefix = ""
)

Arguments

data	Dataset: dataframe containing classification column and all other column features. Both the training and test datasets will be taken from this dataset.
maxnum	Maximum number of trees to be considered. All numbers between 1 and maxnum will be considered. Default: 100.
title	Title to be used for the resulting boxplot
showplots	T if plots should be shown in standard output, F is plots should be saved as jpg files. Default: T.
output_prefix	Prefix used for saving plots. If showplots==F then plots are saved here. Otherwise, standard output.

Value

Dataframe containing test and training accuracy, sensitivity and specificity

Examples

data = read.csv(paste(system.file('samples/subsamples', package = "feamiR"),'/sample0.csv',sep=''))
data = rbind(head(data,50),tail(data,50))
data$classification = as.factor(data$classification)
data = data[,2:ncol(data)]
selectrfnumtrees(data,5,'RF boxplots')
#>    num_trees  accuracy sensitivity specificity  type
#> 1         01 0.9000000   0.9090909   0.8888889  test
#> 2         01 0.9125000   0.8461538   0.9756098 train
#> 3         02 0.7500000   0.9090909   0.5555556  test
#> 4         02 0.9500000   0.9487179   0.9512195 train
#> 5         03 0.8500000   0.0000000   0.6666667  test
#> 6         03 0.9625000   0.9487179   0.9756098 train
#> 7         04 0.9000000   0.9090909   0.8888889  test
#> 8         04 0.9500000   0.9230769   0.9756098 train
#> 9         05 0.9000000   0.9090909   0.8888889  test
#> 10        05 0.9750000   0.9743590   0.9756098 train
#> 11        01 0.8125000   0.8333333   0.8000000  test
#> 12        01 0.9523810   0.9545455   0.9500000 train
#> 13        02 0.8750000   0.8333333   0.9000000  test
#> 14        02 0.9166667   0.9545455   0.8750000 train
#> 15        03 0.9375000   0.0000000   0.9000000  test
#> 16        03 0.9761905   0.9772727   0.9750000 train
#> 17        04 1.0000000   0.0000000   0.0000000  test
#> 18        04 0.9642857   0.9772727   0.9500000 train
#> 19        05 0.8125000   0.0000000   0.7000000  test
#> 20        05 0.9642857   0.9545455   0.9750000 train
#> 21        01 0.8666667   0.8571429   0.8750000  test
#> 22        01 0.9411765   0.9534884   0.9285714 train
#> 23        02 0.8000000   0.8571429   0.7500000  test
#> 24        02 0.9058824   0.9767442   0.8333333 train
#> 25        03 0.9333333   0.8571429   0.0000000  test
#> 26        03 0.9647059   0.0000000   0.9285714 train
#> 27        04 0.8666667   0.8571429   0.8750000  test
#> 28        04 0.9411765   0.9534884   0.9285714 train
#> 29        05 0.8666667   0.8571429   0.8750000  test
#> 30        05 0.9764706   0.0000000   0.9523810 train
#> 31        01 0.8333333   0.9166667   0.7500000  test
#> 32        01 0.9736842   0.9736842   0.9736842 train
#> 33        02 0.9166667   0.8333333   0.0000000  test
#> 34        02 0.9342105   0.8684211   0.0000000 train
#> 35        03 0.9166667   0.9166667   0.9166667  test
#> 36        03 0.9868421   0.0000000   0.9736842 train
#> 37        04 0.8750000   0.9166667   0.8333333  test
#> 38        04 0.9868421   0.0000000   0.9736842 train
#> 39        05 0.9166667   0.9166667   0.9166667  test
#> 40        05 0.9868421   0.0000000   0.9736842 train
#> 41        01 0.9200000   0.0000000   0.8181818  test
#> 42        01 0.9200000   0.0000000   0.8461538 train
#> 43        02 0.8400000   0.7857143   0.9090909  test
#> 44        02 0.9200000   0.9166667   0.9230769 train
#> 45        03 1.0000000   0.0000000   0.0000000  test
#> 46        03 0.9733333   0.9722222   0.9743590 train
#> 47        04 0.9600000   0.9285714   0.0000000  test
#> 48        04 0.9333333   0.8888889   0.9743590 train
#> 49        05 0.9600000   0.0000000   0.9090909  test
#> 50        05 0.9866667   0.0000000   0.9743590 train