inst/doc/introduction.R

In creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(echo = FALSE)
library(creditmodel)

## ----fig.width = 10-----------------------------------------------------------

B_model = training_model(dat = UCICreditCard,
                         model_name = "UCICreditCard",
                         target = "default.payment.next.month",
                         x_list = NULL,
                         occur_time = "apply_date",
                         obs_id = "ID",
                         dat_test = NULL,
                         preproc = TRUE,
                         miss_values = list(-1, -2,"\"\"\"\"",""),
                         missing_proc = TRUE,
                         outlier_proc = TRUE,
                         trans_log = TRUE,
                         feature_filter = list(filter = c("IV", "COR"),
                                               cv_folds = 1,
                                               iv_cp = 0.01,
                                               psi_cp = 0.2,
                                               cor_cp = 0.7,
                                               xgb_cp = 0,
                                               hopper = F),
                         vars_plot = FALSE,
                         algorithm = list("LR"),
                         breaks_list = NULL,
                         LR.params = lr_params(
                           iter = 2,
                           method = 'random_search',
                           tree_control = list(p = 0.02,
                                               cp = c(0.00001, 0.00000001),
                                               xval = 5,
                                               maxdepth = c(10, 15)),
                           bins_control = list(bins_num = 10,
                                               bins_pct = c(0.02, 0.03, 0.05),
                                               b_chi = c(0.01, 0.02, 0.03),
                                               b_odds = 0.1,
                                               b_psi = c(0.02, 0.06),
                                               b_or = c(.05, 0.1, 0.15, 0.2),
                                               mono = c(0.1, 0.2, 0.4, 0.5),
                                               odds_psi = c(0.1, 0.15, 0.2),
                                               kc = 1),
                           f_eval = 'ks',
                           lasso = TRUE,
                           step_wise = FALSE),
                         parallel = FALSE,
                         cores_num = NULL,
                         save_pmml = FALSE,
                         plot_show = TRUE,
                         model_path = tempdir(),
                         seed = 46)