Fit logistic regression model to Weight of Evidence (WOE) data
fits a logistic regression model to the Weight of Evidence (WOE) data and stores
the model predictor names and corresponding coefficients in the
sc
= fitmodel(sc
)creditscorecard
object.
fitmodel
internally transforms all the predictor variables
into WOE values, using the bins found with the automatic or manual binning
process. The response variable is mapped so that "Good" is 1
,
and "Bad" is 0
. This implies that higher (unscaled) scores
correspond to better (less risky) individuals (smaller probability of
default).
Alternatively, you can use setmodel
to provide names of
the predictors that you want in the logistic regression model, along with their
corresponding coefficients.
[
fits a logistic regression model to the Weight of Evidence (WOE) data and stores
the model predictor names and corresponding coefficients in the
sc
,mdl
]
= fitmodel(sc
)creditscorecard
object. fitmodel
returns an updated creditscorecard
object and a
GeneralizedLinearModel
object containing the fitted
model.
fitmodel
internally transforms all the predictor variables
into WOE values, using the bins found with the automatic or manual binning
process. The response variable is mapped so that "Good" is 1
,
and "Bad" is 0
. This implies that higher (unscaled) scores
correspond to better (less risky) individuals (smaller probability of
default).
Alternatively, you can use setmodel
to provide names of
the predictors that you want in the logistic regression model, along with their
corresponding coefficients.
[
fits a logistic regression model to the Weight of Evidence (WOE) data using
optional name-value pair arguments and stores the model predictor names and
corresponding coefficients in the sc
,mdl
]
= fitmodel(___,Name,Value
)creditscorecard
object.
Using name-value pair arguments, you can select which Generalized Linear Model
to fit the data. fitmodel
returns an updated
creditscorecard
object and a
GeneralizedLinearModel
object containing the fitted
model.
[1] Anderson, R. The Credit Scoring Toolkit. Oxford University Press, 2007.
[2] Refaat, M. Credit Risk Scorecards: Development and Implementation Using SAS. lulu.com, 2011.
autobinning
| bindata
| bininfo
| creditscorecard
| displaypoints
| fitConstrainedModel
| fitglm
| formatpoints
| GeneralizedLinearModel
| modifybins
| modifypredictor
| plotbins
| predictorinfo
| probdefault
| score
| setmodel
| stepwiseglm
| validatemodel