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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)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.