creditmodel (version 1.3.1)

partial_dependence_plot: partial_dependence_plot

Description

partial_dependence_plot is for generating a partial dependence plot. get_partial_dependence_plots is for ploting partial dependence of all vairables in x_list.

Usage

partial_dependence_plot(model, x, x_train, n.trees = NULL)
get_partial_dependence_plots(
  model,
  x_train,
  x_list,
  n.trees = NULL,
  dir_path = getwd(),
  save_data = TRUE,
  plot_show = FALSE,
  parallel = FALSE
)

Arguments

model

A data frame of training with predicted prob or score.

x

The name of an independent variable.

x_train

A data.frame with independent variables.

n.trees

Number of trees for best.iter of gbm.

x_list

Names of independent variables.

dir_path

The path for periodically saved graphic files.

save_data

Logical, save results in locally specified folder. Default is FALSE.

plot_show

Logical, show model performance in current graphic device. Default is FALSE.

parallel

Logical, parallel computing. Default is FALSE.

Examples

# NOT RUN {
sub = cv_split(UCICreditCard, k = 30)[[1]]
dat = UCICreditCard[sub,]
dat = re_name(dat, "default.payment.next.month", "target")
dat = data_cleansing(dat, target = "target", obs_id = "ID",
occur_time = "apply_date", miss_values = list("", -1))

train_test = train_test_split(dat, split_type = "OOT", prop = 0.7,
                                occur_time = "apply_date")
dat_train = train_test$train
dat_test = train_test$test
x_list = c("PAY_0", "LIMIT_BAL", "PAY_AMT5", "PAY_3", "PAY_2")
Formula = as.formula(paste("target", paste(x_list, collapse = ' + '), sep = ' ~ '))
set.seed(46)
lr_model = glm(Formula, data = dat_train[, c("target", x_list)], family = binomial(logit))
#plot partial dependency of one variable
partial_dependence_plot(model = lr_model, x ="LIMIT_BAL", x_train = dat_train)
#plot partial dependency of all variables
pd_list = get_partial_dependence_plots(model = lr_model, x_list = x_list[1:2],
 x_train = dat_train, save_data = FALSE,plot_show = TRUE)
# }