decision_tree`()` Builds decision tree on given data. Returns metrics for the tree and optionally plots the tree.

decision_tree`()` Builds decision tree on given data. Returns metrics for the tree and optionally plots the tree.

decision_tree(
  df,
  target = NULL,
  variables = colnames(df),
  classification = TRUE,
  showplot = TRUE,
  maxdepth = 10,
  minsplit = 20,
  cp = 0.01,
  xval = 5,
  seed = 44
)

Arguments

df

Data frame.

target

Column name which values function predicts. By default the last column

variables

column names of given dataframe to process. By default all colnames of dataframe.

classification

If `TRUE`, function builds classification tree, builds regression tree otherwise. If building regression tree and target column is not numeric, casting the column to numeric values. `TRUE` by default.

showplot

If `TRUE` displays decision tree with plot function from rpart.plot package. By default `TRUE`

maxdepth

Maximum depth of decision tree. Default value is `10`

minsplit

The minimum number of observations that must exist in a node in order for a split to be attempted. Default value is `20`

cp

Complexity parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted. The main role of this parameter is to save computing time by pruning off splits that are obviously not worthwhile. Essentially, the user informs the program that any split which does not improve the fit by cp will likely be pruned off by cross-validation, and that hence the program need not pursue it. Default value is `0.01`

xval

Number of cross-validation. Default value is `5`

seed

Random seed, provides repeatability for the tree output. By default `44`

Value

returns a list with calculated metrics for the given predictions and support - number of observations in test and train dataset. These dataframes are splitted using `sample.int` function, using 70/30 proportion. Calculated metrics are:, for classification problem accuracy and balanced accuracy, for regression - RMSE.

Examples

library("toRpEDA")
decision_tree(iris, target = "Species")

#> $train_support
#> [1] 105
#> 
#> $test_support
#> [1] 45
#> 
#> $accuracy
#> [1] 0.9777778
#> 
#> $balanced_accuracy
#> [1] 0.9814815
#> 
decision_tree(USArrests, classification = FALSE)
#> set target value to Rape

#> $train_support
#> [1] 35
#> 
#> $test_support
#> [1] 15
#> 
#> $RMSE
#> [1] 52.99113
#>