Each row corresponds to a rule per trial_nbr
# S3 method for C5.0
tidy(x, ...)C50::C5.0 model fitted with rules = TRUE
Other arguments (See details)
A rulelist object
The output columns are: rule_nbr, trial_nbr, LHS, RHS,
support, confidence, lift.
Rules per trial_nbr are sorted in this order: desc(confidence),
desc(lift), desc(support).
Optional named arguments:
laplace (flag, default: TRUE) is supported. This
computes confidence with laplace correction as documented under 'Rulesets'
here: C5 doc.
rulelist, tidy, augment, predict, calculate, prune, reorder
Other Core Tidy Utility:
tidy(),
tidy.cubist(),
tidy.rpart()
model_c5 = C50::C5.0(Attrition ~., data = modeldata::attrition, rules = TRUE)
tidy(model_c5)
#> ---- Rulelist --------------------------------
#> ▶ Keys: trial_nbr
#> ▶ Number of distinct keys: 1
#> ▶ Number of rules: 24
#> ▶ Model type: C5
#> ▶ Estimation type: classification
#> ▶ Is validation data set: FALSE
#>
#>
#> rule_nbr trial_nbr LHS RHS support confidence lift
#> <int> <int> <chr> <fct> <int> <dbl> <dbl>
#> 1 1 1 ( JobLevel <= 1 ) & ( Mont… Yes 16 0.944 5.9
#> 2 2 1 ( EnvironmentSatisfaction … No 521 0.941 1.1
#> 3 3 1 ( DailyRate <= 722 ) & ( J… Yes 13 0.933 5.8
#> 4 4 1 ( JobRole == 'Research_Sci… No 195 0.924 1.1
#> 5 5 1 ( EnvironmentSatisfaction … Yes 9 0.909 5.6
#> 6 6 1 ( EnvironmentSatisfaction … Yes 9 0.909 5.6
#> 7 7 1 ( JobRole %in% c('Laborato… Yes 14 0.875 5.4
#> 8 8 1 ( JobRole == 'Laboratory_T… Yes 6 0.875 5.4
#> 9 9 1 ( Department == 'Sales' ) … Yes 13 0.867 5.4
#> 10 10 1 ( TotalWorkingYears > 2 ) No 1347 0.864 1
#> # ℹ 14 more rows
#> ----------------------------------------------