predict beforehand as well as possible what will work. But,

How Machine Learning Works And now, here’s the intuitive, elegant answer to the big dilemma, the next step of learning that will move beyond univariate to multivariate predictive modeling, guided by both positive and negative cases: Keep going. So far, we’ve established two risk groups. Next, in the low-risk group, find another factor that best bre

Facebook conducts controlled experiments to see how changes to the rules driving which friends’ posts get displayed influence your engagement and usage of the product.

Induction—Reasoning from detailed facts to general principles. This is not to be confused with deduction, which is essentially the very opposite: Deduction—Reasoning from the general to the particular (or from cause to effect).

care. For many PA projects, the objective is more to predict than it is to understand the world and figure out what makes it tick.

Since it considers only one factor, or predictor variable, about the individual, we call this a univariate model. All the examples in the previous chapter’s tables of bizarre and surprising insights are univariate—they each pertain to one variable such as your salary, your e-mail address, or your credit rating. We need to go multivariate.

Target pulled together training data by merging the baby registry data with other retail customer data, and generated a “fairly accurate” predictive model.

The response rate completely overlooks how many would buy anyway, even if not contacted.

“From the beginning, I thought it was awesome how many people in the top of the leaderboard were what could be called ‘amateurs.’ In fact, our group had no experience with [product recommendations] when we started, either. . . . It just goes to show that sometimes it takes a fresh perspective from outside the field to make progress.”