Chapter 5. Spending Our Data
There are several steps to creating a useful model, including parameter estimation, model selection and tuning, and performance assessment. At the start of a new project, there is usually an initial finite pool of data available for all these tasks, which we can think of as an available data budget. How should the data be applied to different steps or tasks? The idea of data spending is an important first consideration when modeling, especially as it relates to empirical validation.
When data are reused for multiple tasks, instead of being carefully “spent” from the finite data budget, certain risks increase, such as the risk of accentuating bias or compounding effects from methodological errors.
When there are copious amounts of data available, a smart strategy is to allocate specific subsets of data for different tasks, as opposed to allocating the largest possible amount (or even all) to the model parameter estimation only. For example, one possible strategy (when both data and predictors are abundant) is to spend a specific subset of data to determine which predictors are informative, before considering parameter estimation at all. If the initial pool of data available is not huge, there will be some overlap in how and when our data is spent or allocated, and a solid methodology for data spending is important.
This chapter demonstrates the basics of splitting (i.e., creating a data budget) for our initial pool of samples for different ...