Using logistic regression, you can model the relationship between independent variables, or features, and some dependent variable, or outcome. The outcome of logistic regression is always a binary value.
You can build logistic regression models to:
- Fit a predictive model to a training data set of independent variables and some binary dependent variable. Doing so allows you to make predictions on outcomes, such as whether a piece of email is spam mail or not.
- Determine the strength of the relationship between an independent variable and some binary outcome variable. For example, suppose you want to determine whether an email is spam or not. You can build a logistic regression model, based on observations of the properties of email messages. Then, you can determine the importance of various properties of an email message on that outcome.
You can use the following functions to build a logistic regression model, view the model, and use the model to make predictions on a set of test data:
For a complete programming example of how to use logistic regression on a table in Vertica, see Building a Logistic Regression Model.