Description Usage Arguments Value Author(s) Examples
The function takes a logistic model as input and scales the coefficients into scores to be used for scorecard generation. The
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base |
base input dataframe |
target |
column / field name for the target variable to be passed as string (must be 0/1 type) |
model |
input logistic model from which the coefficients are to be picked |
point |
(optional) points after which the log odds will get multiplied by "factor" (default value is 15) |
factor |
(optional) factor by which the log odds must get multiplied after a step of "points" (default value is 2) |
setscore |
(optional) input for setting offset (default value is 660) |
The function returns a dataframe with the coefficients and scalled scores for each class of all explanatory variables of the model.
Arya Poddar <aryapoddar290990@gmail.com>
1 2 3 4 5 6 7 8 9 | data <- iris
suppressWarnings(RNGversion('3.5.0'))
set.seed(11)
data$Y <- sample(0:1,size=nrow(data),replace=TRUE)
x <- c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")
iv_table_list <- iv_table(base = data,target = "Y",num_var_name = x,cat_var_name = "Species")
num_cat <- num_to_cat(base = data,num_woe_table = iv_table_list$num_woe_table)
log_model <- glm(Y ~ ., data = num_cat, family = "binomial")
scaling_tab <- scalling(base = num_cat,target = "Y",model = log_model)
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