Fundamental concepts: Careful consideration of what is desired from data science results; Expected value as a key evaluation framework; Consideration of appropriate comparative baselines.
Exemplary techniques: Various evaluation metrics; Estimating costs and benefits; Calculating expected profit; Creating baseline methods for comparison.
Recall from the beginning of Chapter 5: as a manager at MegaTelCo, you wanted to assess whether the model my consulting firm had produced was any good. Overfitting aside, how would you go about measuring that?
For data science to add value to an application, it is important for the data scientists and other stakeholders to consider carefully what they would like to achieve by mining data. This sounds obvious, so it is sometimes surprising how often it is ignored. Both data scientists themselves and the people who work with them often avoid—perhaps without even realizing it—connecting the results of mining data back to the goal of the undertaking. This may manifest itself in the reporting of a statistic without a clear understanding of why it is the right statistic, or in the failure to figuring out how to measure performance in a meaningful way.
We should be careful with such a criticism, though. Often it is not possible to measure perfectly one’s ultimate goal, for example because the systems are inadequate, or because it is too costly to gather the right data, or because it is difficult ...