Chapter 5. Overfitting and Its Avoidance
Fundamental concepts: Generalization; Fitting and overfitting; Complexity control.
Exemplary techniques: Cross-validation; Attribute selection; Tree pruning; Regularization.
One of the most important fundamental notions of data science is that of overfitting and generalization. If we allow ourselves enough flexibility in searching for patterns in a particular dataset, we will find patterns. Unfortunately, these “patterns” may be just chance occurrences in the data. As discussed previously, we are interested in patterns that generalize—that predict well for instances that we have not yet observed. Finding chance occurrences in data that look like interesting patterns, but which do not generalize, is called overfitting the data.
Consider the following (extreme) example. You’re a manager at MegaTelCo, responsible for reducing customer churn. I run a data mining consulting group. You give my data science team a set of historical data on customers who have stayed with the company and customers who have departed within six months of contract expiration. My job is to build a model to distinguish customers who are likely to churn based on some features, as we’ve discussed previously. I mine the data and build a model. I give you back the code for the model, to implement in your company’s churn-reduction system.
Of course you are interested in whether my model is any good, so you ask your technical team to check the performance of the model ...