The main idea behind Classification is that you are trying to predict or understand one variable by making use of other variables. For example, you may be trying to predict which customers are likely to apply for a credit card, and you are trying to predict this from the customers' demographic and financial variables. Hence, in this scenario there are two types of variables:
- The target variable: This is a variable that you are trying to predict or understand
- The input variables: These are variables that you are using to try to predict or understand the target variable
The ultimate goal of this type of analysis is predictive accuracy. In this course, we will only have time to cover a couple of predictive models. Modeler offers a great number of predictive models. This section gives an overview, distinguishing between three classes of classification models, which are listed as follows:
- Rule induction models
- Statistical models
- Machine learning models
Rule induction models are an important...
