Abstract
Data preprocessing techniques generally refer to the addition, deletion, or transformation of the training set data. Preprocessing data is a crucial step prior to modeling since data preparation can make or break a model’s predictive ability. To illustrate general preprocessing techniques, we begin by introducing a cell segmentation data set (Section 3.1). This data set contains common predictor problems such as skewness, outliers, and missing values. Sections 3.2 and 3.3 review predictor transformations for single predictors and multiple predictors, respectively. In Section 3.4 we discuss several approaches for handling missing data. Other preprocessing steps may include removing (Section 3.5), adding (Section 3.6), or binning (Section 3.7) predictors, all of which must be done carefully so that predictive information is not lost or erroneous information is added to the data. The computing section (3.8) provides R syntax for the previously described preprocessing steps. Exercises are provided at the end of the chapter to solidify concepts.
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- 1.
The individual data points can be found on the journal web site or in the RAppliedPredictiveModeling package. See the Computing section at the end of this chapter.
- 2.
The original authors included several “status” features that are binary representations of other features in the data set. We excluded these from the analysis in this chapter.
- 3.
Some readers familiar with Box and Cox (1964) will know that this transformation was developed for outcome data while Box and Tidwell (1962) describe similar methods for transforming a set of predictors in a linear model. Our experience is that the Box–Cox transformation is more straightforward, less prone to numerical issues, and just as effective for transforming individual predictor variables.
- 4.
Section 20.5 discusses model extrapolation—where the model predicts samples outside of the mainstream of the training data. Another concept is the applicability domain of the model, which is the population of samples that can be effectively predicted by the model.
- 5.
A preprocessed version of these data can also be found in the caret package and is used in later chapters.
- 6.
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Kuhn, M., Johnson, K. (2013). Data Pre-processing. In: Applied Predictive Modeling. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6849-3_3
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