You have just modeled the input data using decision trees, which are nonparametric.
As part of your analysis, you now perform the following tasks in order to also
model the data using parametric methods:
-
You impute values to use as replacements for missing
values that are in the input data. Regressions and neural networks
would otherwise ignore missing values, which would decrease the amount
of data that you use in the models and lower their predictive power.
-
You transform input variables to make the usual assumptions
of regression more appropriate for the input data.
-
You model the input data using logistic regression,
a statistical method with which your management is familiar.
-
You model the input data using neural networks, which
are more flexible than logistic regression (and more complicated).