After installing scorecard via instructions in the README section, load the package into your environment.

Let’s use the *germancredit* dataset for the purposes of this
demonstration.

The `var_filter`

function drops column variables that
don’t meet the thresholds for missing rate (> 95% by default),
information value (IV) (< 0.02 by default), or identical value rate
(> 95% by default).

`dt_f <- var_filter(germancredit, y = "creditability")`

When building scorecard models, a subset of the observations should
be held out from the data used to train the model (similar to most other
traditional modeling approaches), and instead be apportioned to the
*test* set. We can perform this sampling to create the
*train* and *test* datasets using the
`split_df`

function.

Weight-of-Evidence binning is a technique for binning both continuous
and categorical independent variables in a way that provides the most
robust bifurcation of the data against the dependent variable. This
technique can be easily executed across all independent variables using
the `woebin`

function.

```
bins <- woebin(dt_f, y = "creditability")
# woebin_plot(bins)
```

The user can also adjust bin breaks interactively by using the
`woebin_adj`

function.

`# breaks_adj <- woebin_adj(dt_f, y = "creditability", bins = bins)`

Furthermore, the user can set the bin breaks manually via the
`breaks_list = list()`

argument in the `woebin`

function. Note the use of *%,%* as a separator to create a single
bin from two classes in a categorical independent variable.

```
breaks_adj <- list(
age.in.years = c(26, 35, 40),
other.debtors.or.guarantors = c("none", "co-applicant%,%guarantor")
)
bins_adj <- woebin(dt_f, y = "creditability", breaks_list = breaks_adj)
```

Once your WoE bins are established for all desired independent variables, apply the binning logic to the training and test datasets.

`dt_woe_list <- lapply(dt_list, function(x) woebin_ply(x, bins_adj))`

Logistic regression can often be leveraged effectively to assist in building the scorecards.

```
m1 <- glm( creditability ~ ., family = binomial(), data = dt_woe_list$train)
# vif(m1, merge_coef = TRUE) # summary(m1)
# Select a formula-based model by AIC (or by LASSO for large dataset)
m_step <- step(m1, direction = "both", trace = FALSE)
m2 <- eval(m_step$call)
# vif(m2, merge_coef = TRUE) # summary(m2)
```

If oversampling is a concern, the following code chunk could be uncommented and run to help adjust for this issue.

```
# Read documentation on handling oversampling (support.sas.com/kb/22/601.html)
# library(data.table)
# p1 <- 0.03 # bad probability in population
# r1 <- 0.3 # bad probability in sample dataset
# dt_woe <- copy(dt_woe_list$train)[, weight := ifelse(creditability == 1, p1/r1, (1-p1)/(1-r1) )][]
# fmla <- as.formula(paste("creditability ~", paste(names(coef(m2))[-1], collapse = "+")))
# m3 <- glm(fmla, family = binomial(), data = dt_woe, weights = weight)
```

The `perf_eva`

function provides model accuracy statistics
(such as mse, rmse, logloss, r2, ks, auc, gini) and plots (such as ks,
lift, gain, roc, lz, pr, f1, density).

Once the model has been selected, scorecards can be created via the
`scorecard`

function. Note that the default target points is
600, target odds is 1/19 and points to double the odds is 50. See
`?scorecard`

for more information on the function and its
arguments.

The scorecard can then be applied to the original data using the
`scorecard_ply`

function. Lastly, a chart encompassing
Population Stability Index (PSI) statistics can be rendered via the
`perf_psi`

function.

```
# Build the card
card <- scorecard(bins_adj, m2)
# Obtain Credit Scores
score_list <- lapply(dt_list, function(x) scorecard_ply(x, card))
# Analyze the PSI
perf_psi(score = score_list, label = label_list)
```