creditmodel is a free and open source automated modeling R package designed to help model developers improve model development efficiency and enable many people with no background in data science to complete the modeling work in a short time.Let them focus more on the problem itself and allocate more time to decision-making.
creditmodel covers various tools such as data preprocessing, variable processing/derivation, variable screening/dimensionality reduction, modeling, data analysis, data visualization, model evaluation, strategy analysis, etc. It is a set of customized “core” tool kit for model developers.
creditmodel is suitable for machine learning automated modeling of classification targets, and is more suitable for the risk and marketing data of financial credit, e-commerce, and insurance with relatively high noise and low information content.
# install.packages("creditmodel") # Automated Model Development Process
if (!dir.exists("c:/test_model")) dir.create("c:/test_model")
setwd("c:/test_model")
library(creditmodel)
sub = cv_split(UCICreditCard, k = 3)[[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, -2))
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
B_model = training_model(dat = dat_train,
model_name = "UCICreditCard", target = "target", x_list = NULL,
occur_time = "apply_date", obs_id = "ID", dat_test = dat_test,
preproc = FALSE,
feature_filter = NULL,
algorithm = list("RF","LR","XGB","GBM"),
LR.params = lr_params(lasso = TRUE,
step_wise = FALSE, vars_plot = FALSE),
XGB.params = xgb_params(),
breaks_list = NULL,
parallel = FALSE, cores_num = NULL,
save_pmml = FALSE, plot_show = FALSE,
model_path = getwd(),
seed = 46)