In Chapter 6, Building a Classification Model with Spark, we showed how feature transformation and selection can make a large difference to the performance of a model. In this chapter, we will focus on another type of transformation that can be applied to a dataset: transforming the target variable itself.
Improving model performance and tuning parameters
Transforming the target variable
Recall that many machine learning models, including linear models, make assumptions regarding the distribution of the input data as well as target variables. In particular, linear regression assumes a normal distribution.
In many real-world cases, the distributional assumptions of linear regression do not hold. In this case, for example, we know that the number of bike rentals can...
