The Model object contains all of the information describing a fitted binnr Scorecard. This includes a list of the Transforms used for WoE substitution, which variables entered the model, and some performance metrics.

`name`

short model identifier

`description`

short description of the model

`settings`

saved list of settings used to bin and fit the model

`transforms`

list of Bin Transforms used to fit the model

`dropped`

character vector denoting which variables were dropped at time of fit

`inmodel`

character vector denoting which variables entered the model

`steptwo`

named numeric vector of how varaibles would enter the model if the lambda value was lowered (a "looser" fitting model).

`coefs`

intercept and coefficients of the fitted model as a numeric vector

`contribution`

some measure of variable contribution to the model's predictive power

`ks`

statistic measuring the separation of the two performance classes

`fit`

the result of the cv.glmnet function call

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