train_lr {creditmodel}  R Documentation 
Trainig LR model
Description
train_lr
is for training the logistic regression model using in training_model
.
Usage
train_lr(
dat_train,
dat_test = NULL,
target,
x_list = NULL,
occur_time = NULL,
prop = 0.7,
tree_control = list(p = 0.02, cp = 1e08, xval = 5, maxdepth = 10),
bins_control = list(bins_num = 10, bins_pct = 0.05, b_chi = 0.02, b_odds = 0.1, b_psi
= 0.03, b_or = 0.15, mono = 0.2, odds_psi = 0.15, kc = 1),
thresholds = list(cor_p = 0.8, iv_i = 0.02, psi_i = 0.1, cos_i = 0.6),
lasso = TRUE,
step_wise = TRUE,
best_lambda = "lambda.auc",
seed = 1234,
...
)
Arguments
dat_train 
data.frame of train data. Default is NULL. 
dat_test 
data.frame of test data. Default is NULL. 
target 
name of target variable. 
x_list 
names of independent variables. Default is NULL. 
occur_time 
The name of the variable that represents the time at which each observation takes place.Default is NULL. 
prop 
Percentage of traindata after the partition. Default: 0.7. 
tree_control 
the list of parameters to control cutting initial breaks by decision tree. See details at: 
bins_control 
the list of parameters to control merging initial breaks. See details at: 
thresholds 
Thresholds for selecting variables.

lasso 
Logical, if TRUE, variables filtering by LASSO. Default is TRUE. 
step_wise 
Logical, stepwise method. Default is TRUE. 
best_lambda 
Metheds of best lanmbda stardards using to filter variables by LASSO. There are 3 methods: ("lambda.auc", "lambda.ks", "lambda.sim_sign") . Default is "lambda.auc". 
seed 
Random number seed. Default is 1234. 
... 
Other parameters 