What are the some of the differences between the various types of predictive models?
First, there is no one best model. Different data requires different types of models. The accuracy of a model depends more on the quality of the data, how well it is prepared, and how fresh the model is than on the type of model used. On the other hand, there are some important differences between different types of models. Nonlinear models are generally more accurate than linear models. Linear models were more common in the past because they were easier to compute. Today this is no longer relevant given the proliferation of computers and good quality statistical and data mining software. Neural networks were very popular in the 80’s and early 90’s because they were quite successful for several different types of applications and because they had a cool name. Today, a variety of other methods are also commonly used, including tree-based methods and support vector machines. For example, tree-based methods are generally considered easier to build, easier to interpret, and more scalable t
