lr_params {creditmodel}  R Documentation 
lr_params
is the list of parameters to train a LR model or Scorecard using in training_model
.
lr_params_search
is for searching the optimal parameters of logistic regression,if any parameters of params in lr_params
is more than one.
lr_params(
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),
f_eval = "ks",
best_lambda = "lambda.ks",
method = "random_search",
iters = 10,
lasso = TRUE,
step_wise = TRUE,
score_card = TRUE,
sp_values = NULL,
forced_in = NULL,
obsweight = c(1, 1),
thresholds = list(cor_p = 0.8, iv_i = 0.02, psi_i = 0.1, cos_i = 0.5),
...
)
lr_params_search(
method = "random_search",
dat_train,
target,
dat_test = NULL,
occur_time = NULL,
x_list = NULL,
prop = 0.7,
iters = 10,
tree_control = list(p = 0.02, cp = 0, xval = 1, maxdepth = 10),
bins_control = list(bins_num = 10, bins_pct = 0.02, b_chi = 0.02, b_odds = 0.1, b_psi
= 0.05, b_or = 0.1, mono = 0.1, odds_psi = 0.03, kc = 1),
thresholds = list(cor_p = 0.8, iv_i = 0.02, psi_i = 0.1, cos_i = 0.6),
step_wise = FALSE,
lasso = FALSE,
f_eval = "ks"
)
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: 
f_eval 
Custimized evaluation function, "ks" & "auc" are available. 
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". 
method 
Method of searching optimal parameters. "random_search","grid_search","local_search" are available. 
iters 
Number of iterations of "random_search" optimal parameters. 
lasso 
Logical, if TRUE, variables filtering by LASSO. Default is TRUE. 
step_wise 
Logical, stepwise method. Default is TRUE. 
score_card 
Logical, transfer woe to a standard scorecard. If TRUE, Output scorecard, and score prediction, otherwise output probability. Default is TRUE. 
sp_values 
Vaules will be in separate bins.e.g. list(1, "missing") means that 1 & missing as special values.Default is NULL. 
forced_in 
Names of forced input variables. Default is NULL. 
obsweight 
An optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector. If you oversample or cluster diffrent datasets to training the LR model, you need to set this parameter to ensure that the probability of logistic regression output is the same as that before oversampling or segmentation. e.g.:There are 10,000 0 obs and 500 1 obs before oversampling or undersampling, 5,000 0 obs and 3,000 1 obs after oversampling. Then this parameter should be set to c(10000/5000, 500/3000). Default is NULL.. 
thresholds 
Thresholds for selecting variables.

... 
Other parameters 
dat_train 
data.frame of train data. Default is NULL. 
target 
name of target variable. 
dat_test 
data.frame of test data. Default is NULL. 
occur_time 
The name of the variable that represents the time at which each observation takes place.Default is NULL. 
x_list 
names of independent variables. Default is NULL. 
prop 
Percentage of traindata after the partition. Default: 0.7. 
A list of parameters.
training_model
, xgb_params
, gbm_params
, rf_params