creditmodel (version 1.3.1)

model_result_plot: model result plots model_result_plot is a wrapper of following: perf_table is for generating a model performance table. ks_plot is for K-S. roc_plot is for ROC. lift_plot is for Lift Chart. score_distribution_plot is for ploting the score distribution.

Description

model result plots model_result_plot is a wrapper of following: perf_table is for generating a model performance table. ks_plot is for K-S. roc_plot is for ROC. lift_plot is for Lift Chart. score_distribution_plot is for ploting the score distribution.

performance table

ks_plot

lift_plot

roc_plot

score_distribution_plot

Usage

model_result_plot(
  train_pred,
  score,
  target,
  test_pred = NULL,
  gtitle = NULL,
  perf_dir_path = NULL,
  save_data = FALSE,
  plot_show = TRUE,
  total = TRUE,
  g = 10,
  cut_bin = "equal_depth",
  digits = 4
)
perf_table(
  train_pred,
  test_pred = NULL,
  target = NULL,
  score = NULL,
  g = 10,
  cut_bin = "equal_depth",
  breaks = NULL,
  digits = 2,
  pos_flag = list("1", "1", "Bad", 1),
  total = FALSE,
  binsNO = FALSE
)
ks_plot(
  train_pred,
  test_pred = NULL,
  target = NULL,
  score = NULL,
  gtitle = NULL,
  breaks = NULL,
  g = 10,
  cut_bin = "equal_width",
  perf_tb = NULL
)
lift_plot(
  train_pred,
  test_pred = NULL,
  target = NULL,
  score = NULL,
  gtitle = NULL,
  breaks = NULL,
  g = 10,
  cut_bin = "equal_depth",
  perf_tb = NULL
)
roc_plot(
  train_pred,
  test_pred = NULL,
  target = NULL,
  score = NULL,
  gtitle = NULL
)
score_distribution_plot(
  train_pred,
  test_pred,
  target,
  score,
  gtitle = NULL,
  breaks = NULL,
  g = 10,
  cut_bin = "equal_depth",
  perf_tb = NULL
)

Arguments

train_pred

A data frame of training with predicted prob or score.

score

The name of prob or score variable.

target

The name of target variable.

test_pred

A data frame of validation with predict prob or score.

gtitle

The title of the graph & The name for periodically saved graphic file.

perf_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 TRUE.

total

Whether to summarize the table. default: TRUE.

g

Number of breaks for prob or score.

cut_bin

A string, if equal_bins is TRUE, 'equal_depth' or 'equal_width', default is 'equal_depth'.

digits

Digits of numeric,default is 4.

breaks

Splitting points of prob or score.

pos_flag

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

binsNO

Bins Number.Default is FALSE.

perf_tb

Performance table.

Examples

# NOT RUN {
sub = cv_split(UCICreditCard, k = 30)[[1]]
dat = UCICreditCard[sub,]
dat = re_name(dat, "default.payment.next.month", "target")
x_list = c("PAY_0", "LIMIT_BAL", "PAY_AMT5", "PAY_3", "PAY_2")
dat = data_cleansing(dat, target = "target", obs_id = "ID",x_list = x_list,
occur_time = "apply_date", miss_values = list("", -1))
dat = process_nas(dat,default_miss = TRUE)
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
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
perf_table(train_pred = dat_train, test_pred = dat_test, target = "target", score = "pred_LR")
ks_plot(train_pred = dat_train, test_pred = dat_test, target = "target", score = "pred_LR")
roc_plot(train_pred = dat_train, test_pred = dat_test, target = "target", score = "pred_LR")
#lift_plot(train_pred = dat_train, test_pred = dat_test, target = "target", score = "pred_LR")
#score_distribution_plot(train_pred = dat_train, test_pred = dat_test,
#target = "target", score = "pred_LR")
#model_result_plot(train_pred = dat_train, test_pred = dat_test,
#target = "target", score = "pred_LR")
# }