Scorecard Modeling Report
report creates a scorecard modeling report and save it as a xlsx file.
report(dt, y, x, breaks_list, special_values = NULL, seed = 618, save_report = "report", positive = "bad|1", ...)
dt |
A data frame or a list of data frames that have both x (predictor/feature) and y (response/label) variables. If there are multiple data frames are provided, only the first data frame would be used for training, and the others would be used for testing/validation. |
y |
Name of y variable. |
x |
Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. |
breaks_list |
A list of break points. It can be extracted from |
special_values |
The values specified in special_values will be in separate bins. Defaults to NULL. |
seed |
A random seed to split input data frame. Defaults to 618. If it is NULL, input dt will not split into two datasets. |
save_report |
The name of xlsx file where the report is to be saved. Defaults to 'report'. |
positive |
Value of positive class, default "bad|1". |
... |
Additional parameters. |
## Not run:
data("germancredit")
y = 'creditability'
x = c(
"status.of.existing.checking.account",
"duration.in.month",
"credit.history",
"purpose",
"credit.amount",
"savings.account.and.bonds",
"present.employment.since",
"installment.rate.in.percentage.of.disposable.income",
"personal.status.and.sex",
"property",
"age.in.years",
"other.installment.plans",
"housing"
)
special_values=NULL
breaks_list=list(
status.of.existing.checking.account=c("... < 0 DM%,%0 <= ... < 200 DM",
"... >= 200 DM / salary assignments for at least 1 year", "no checking account"),
duration.in.month=c(8, 16, 34, 44),
credit.history=c(
"no credits taken/ all credits paid back duly%,%all credits at this bank paid back duly",
"existing credits paid back duly till now", "delay in paying off in the past",
"critical account/ other credits existing (not at this bank)"),
purpose=c("retraining%,%car (used)", "radio/television",
"furniture/equipment%,%domestic appliances%,%business%,%repairs",
"car (new)%,%others%,%education"),
credit.amount=c(1400, 1800, 4000, 9200),
savings.account.and.bonds=c("... < 100 DM", "100 <= ... < 500 DM",
"500 <= ... < 1000 DM%,%... >= 1000 DM%,%unknown/ no savings account"),
present.employment.since=c("unemployed%,%... < 1 year", "1 <= ... < 4 years",
"4 <= ... < 7 years", "... >= 7 years"),
installment.rate.in.percentage.of.disposable.income=c(2, 3),
personal.status.and.sex=c("male : divorced/separated", "female : divorced/separated/married",
"male : single", "male : married/widowed"),
property=c("real estate", "building society savings agreement/ life insurance",
"car or other, not in attribute Savings account/bonds", "unknown / no property"),
age.in.years=c(26, 28, 35, 37),
other.installment.plans=c("bank%,%stores", "none"),
housing=c("rent", "own", "for free")
)
# Example I
# input dt is a data frame
# split input data frame into two
report(germancredit, y, x, breaks_list, special_values, seed=618, save_report='report1',
show_plot = c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density'))
# donot split input data
report(germancredit, y, x, breaks_list, special_values, seed=NULL, save_report='report2')
# Example II
# input dt is a list
# only one dataset
report(list(dt=germancredit), y, x,
breaks_list, special_values, seed=NULL, save_report='report3')
# multiple datasets
report(list(dt1=germancredit[sample(1000,500)],
dt2=germancredit[sample(1000,500)]), y, x,
breaks_list, special_values, seed=NULL, save_report='report4')
# multiple datasets
report(list(dt1=germancredit[sample(1000,500)],
dt2=germancredit[sample(1000,500)],
dt3=germancredit[sample(1000,500)]), y, x,
breaks_list, special_values, seed=NULL, save_report='report5')
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.