varImp.C5.0: Variable Importance Measures for C5.0 Models

In C50: C5.0 Decision Trees and Rule-Based Models

Description

This function calculates the variable importance (aka attribute usage) for C5.0 models.

Usage

C5imp(object, metric = "usage", pct = TRUE, ...)

Arguments

object	an object of class C5.0
metric	either 'usage' or 'splits' (see Details below)
pct	a logical: should the importance values be converted to be between 0 and 100?
...	other options (not currently used)

Details

By default, C5.0 measures predictor importance by determining the percentage of training set samples that fall into all the terminal nodes after the split (this is used when metric = "usage"). For example, the predictor in the first split automatically has an importance measurement of 100 percent. Other predictors may be used frequently in splits, but if the terminal nodes cover only a handful of training set samples, the importance scores may be close to zero. The same strategy is applied to rule-based models as well as the corresponding boosted versions of the model.

There is a difference in the attribute usage numbers between this output and the nominal command line output. Although the calculations are almost exactly the same (we do not add 1/2 to everything), the C code does not display that an attribute was used if the percentage of training samples covered by the corresponding splits is very low. Here, the threshold was lowered and the fractional usage is shown.

When metric = "splits", the percentage of splits associated with each predictor is calculated.

Value

a data frame with a column Overall with the predictor usage values. The row names indicate the predictor.

Author(s)

Original GPL C code by Ross Quinlan, R code and modifications to C by Max Kuhn, Steve Weston and Nathan Coulter

References

Quinlan R (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, http://www.rulequest.com/see5-unix.html

See Also

C5.0, C5.0Control, summary.C5.0, predict.C5.0

Examples

data(churn)

treeModel <- C5.0(x = churnTrain[, -20], y = churnTrain$churn)
C5imp(treeModel)
C5imp(treeModel, metric = "splits")

Example output

                              Overall
total_day_minutes              100.00
number_customer_service_calls   93.67
international_plan              87.73
total_eve_charge                20.73
voice_mail_plan                  8.97
total_intl_calls                 8.01
total_intl_minutes               6.48
total_night_minutes              6.33
total_eve_minutes                4.74
total_eve_calls                  0.57
account_length                   0.18
total_day_charge                 0.18
state                            0.00
area_code                        0.00
number_vmail_messages            0.00
total_day_calls                  0.00
total_night_calls                0.00
total_night_charge               0.00
total_intl_charge                0.00
                                Overall
total_day_minutes             26.923077
total_eve_charge              15.384615
total_night_minutes           15.384615
international_plan             7.692308
voice_mail_plan                7.692308
account_length                 3.846154
number_customer_service_calls  3.846154
total_day_charge               3.846154
total_eve_calls                3.846154
total_eve_minutes              3.846154
total_intl_calls               3.846154
total_intl_minutes             3.846154
state                          0.000000
area_code                      0.000000
number_vmail_messages          0.000000
total_day_calls                0.000000
total_night_calls              0.000000
total_night_charge             0.000000
total_intl_charge              0.000000

C50 documentation built on May 2, 2019, 4:47 p.m.