Society of Actuaries (SOA) PA Practice Exam

What is a potential disadvantage of using binarization on factor variables?

1. It can increase model complexity

2. It leads to better convergence in models

3. It simplifies the factor analysis process

4. It automatically identifies significant levels

The correct answer is: It can increase model complexity

Binarization of factor variables, often used in modeling techniques, transforms categorical data into a binary format, typically resulting in multiple binary variables representing each level of the original variable. This transformation can indeed increase model complexity because it adds more parameters to the model, especially when a factor variable has many levels. Each level gets its own binary variable, which can lead to a high-dimensional dataset. This increase in complexity can make it more challenging to interpret the model, increase computational demands, and even lead to problems such as overfitting if the model becomes too complex relative to the number of observations available. The other choices do not accurately describe the disadvantages of binarization. For instance, asserting that it leads to better convergence in models is misleading; while binarization might aid some models, it does not inherently guarantee improved convergence. Similarly, binarization tends to complicate rather than simplify the factor analysis process because it transforms the data structure, which can require careful consideration in subsequent analysis. Finally, automatically identifying significant levels is not a function of binarization; rather, analysis techniques need to be applied after binarization to determine significance, adding further complexity to the task. Thus, the correct option highlights the key disadvantage of increased model complexity stemming from