Chapter 1: Examining the Distribution of Features and Targets
Machine learning writing and instruction are often algorithm-focused. Sometimes, this gives the impression that all we have to do is choose the right model and that organization-changing insights will follow. But the best place to begin a machine learning project is with an understanding of how the features and targets we will use are distributed.
It is important to make room for the same kind of learning from data that has been central to our work as analysts for decades – studying the distribution of variables, identifying anomalies, and examining bivariate relationships – even as we focus more and more on the accuracy of our predictions.
We will explore tools for doing so in the ...
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