Sometimes our models overfit, sometimes they overfit.
A model’s capacity is, informally, its ability to fit a wide variety of functions. As a simple example, a linear regression model with a single parameter has a much lower capacity than a linear regression model with multiple polynomial parameters. Different datasets demand models of different capacity, and each time we apply a model to a dataset we run the risk of overfitting or underfitting our data.
Continue reading “Statistical Learning Theory: VC Dimension, Structural Risk Minimization”
Nick Ryan 