The goal of meta-learning is to model the relationships between the performance of various learning algorithms and the characteristics of problems being learned. In this sense, we are focused on learning about learning. Under what conditions can we expect a certain algorithm to perform well? The field of meta-learning has been very well developed in the machine learning community over the last 15 years or so, where the focus has been on the study of supervised learning methods such as support vector machines and neural networks, and their performance on classification problems. But the goal of seeking a greater understanding of the relationship between problem characteristics and algorithm performance is not limited to machine learning or classification problems.