ks_table {creditmodel}

ks_table & plot

Description

ks_table is for generating a model performance table. ks_table_plot is for ploting the table generated by ks_table ks_psi_plot is for K-S & PSI distrbution ploting.

Usage

ks_table(
  train_pred,
  test_pred = NULL,
  target = NULL,
  score = NULL,
  g = 10,
  breaks = NULL,
  pos_flag = list("1", "1", "Bad", 1)
)

ks_table_plot(
  train_pred,
  test_pred,
  target = "target",
  score = "score",
  g = 10,
  plot_show = TRUE,
  g_width = 12,
  file_name = NULL,
  save_data = FALSE,
  dir_path = tempdir(),
  gtitle = NULL
)

ks_psi_plot(
  train_pred,
  test_pred,
  target = "target",
  score = "score",
  gtitle = NULL,
  plot_show = TRUE,
  g_width = 12,
  save_data = FALSE,
  breaks = NULL,
  g = 10,
  dir_path = tempdir()
)

model_key_index(tb_pred)

Arguments

train_pred

A data frame of training with predicted prob or score.

test_pred

A data frame of validation with predict prob or score.

target

The name of target variable.

score

The name of prob or score variable.

g

Number of breaks for prob or score.

breaks

Splitting points of prob or score.

pos_flag

The value of positive class of target variable, default: "1".

plot_show

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

g_width

Width of graphs.

file_name

The name for periodically saved data file. Default is NULL.

save_data

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

dir_path

The path for periodically saved graphic files.

gtitle

The title of the graph & The name for periodically saved graphic file. Default is "_ks_psi_table".

tb_pred

A table generated by codeks_table

Examples

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))

dat_train$pred_LR = round(predict(lr_model, dat_train[, x_list], type = "response"), 5)
dat_test$pred_LR = round(predict(lr_model, dat_test[, x_list], type = "response"), 5)
# model evaluation
ks_psi_plot(train_pred = dat_train, test_pred = dat_test,
                            score = "pred_LR", target = "target",
                            plot_show = TRUE)
tb_pred = ks_table_plot(train_pred = dat_train, test_pred = dat_test,
                                        score = "pred_LR", target = "target",
                                     g = 10, g_width = 13, plot_show = FALSE)
key_index = model_key_index(tb_pred)