Linear models are one of the most widely used models and form the foundation of many advanced nonlinear techniques such as support vector machines and neural networks. They can be applied to any predictive task such as classification, regression, or probability estimation.
When responding to small changes in the input data, and provided that our data consists of entirely uncorrelated features, linear models tend to be more stable than tree models. As we mentioned in the last chapter, tree models can over-respond to small variations in training data. This is because splits at the root of a tree have consequences that are not recoverable further down the line, that is, producing different branching and potentially making the rest of the tree significantly different. Linear models on the other hand are relatively stable, being less sensitive to initial conditions. However, as you would expect, this has the opposite effect, changing less sensitive data to nuanced data...