Predictive Analytics

Most learning methods search for a good predictive model, starting with a trivially simple and often inept model and tweaking it repeatedly, as if applying “genetic mutations,” until it evolves into a robust prediction apparatus. In the case of a decision tree, the process starts with a small tree and grows it. In the case of most mathematical equa

Among the competing approaches to machine learning, decision trees are often considered the most user friendly, since they consist of rules you can read like a long (if cumbersome) English sentence, while other methods are more mathy, taking the variables and plugging them into equations.

Beyond persuasion modeling, the team also employed predictive modeling to predict the propensity to vote for Obama regardless of campaign contact, the probability of voting at all (turnout), and the probability of donating in order to target fund-raising efforts.

Remember, the predictive modeling process is a form of automated data crunching that learns from training examples, which must include both positive and negative examples.

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.

After all, if a rule in the tree (formed by following a path from root to leaf) references many variables, it can eliminate all but one individual. But such a rule isn’t general; it applies to only one case. Believing in such a rule is accepting proof by example. In this way, a large tree could essentially memorize the entire training data. You’ve

Following the open data movement, often embracing a not-for-profit philosophy, many data sets are available online from fields like biodiversity, business, cartography, chemistry, genomics, and medicine. Look at one central index, www.kdnuggets.com/datasets, and you’ll see what amounts to lists of lists of data resources.

predicts forthcoming forms of fraud by generalizing from previously observed examples. This is the defining characteristic of a learning system.

To put names on the mathy methods that compete with decision trees, they include artificial neural networks, loglinear regression, support vector machines, and TreeNet.

Unlike the social sciences, PA’s objective is to improve operational efficiency rather than figure people out for its own sake—and, either way, just because you’re observing a person does not mean that person is being treated like an animal.