Chapter 6
Impute and Transform, Build
Neural Networks, and Build a
Regression Model
About the Tasks That You Will Perform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Impute Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Transform Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Analyze with a Logistic Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Analyze with a Neural Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
About the Tasks That You Will Perform
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:
1. 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.
2. You transform input variables to make the usual assumptions of regression more
appropriate for the input data.
3. You model the input data using logistic regression, a statistical method with which
your management is familiar.
4. You model the input data using neural networks, which are more flexible than
logistic regression (and more complicated).
Impute Missing Values
For decision trees, missing values are not problematic. Surrogate splitting rules enable
you to use the values of other input variables to perform a split for observations with
missing values. In SAS Enterprise Miner, however, models such as regressions and
neural networks ignore altogether observations that contain missing values, which
reduces the size of the training data set. Less training data can substantially weaken the
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