As more businesses embrace data and the information at their disposal, they are looking into mechanisms and models that can turn that information into sufficient evidence to back business decisions. That’s where logistic regression models come into play, allowing organizations to garner an idea about the probability of an event, as machine learning outlines predictions of what’s to come next. Let’s take a closer look at what logistic regression truly means and where it can be applied for various industries.
Understanding Logistic Regression
Having a statistical model for predicted probability can help companies across various sectors prepare for what’s to come. That’s the heart of logistical regression. Logistic regression, or LR, shows the relationship between features and then calculates the probability of an outcome. LR helps to create accurate predictions through machine learning. It’s similar to linear regression, except rather than a graphical result, the target variable is binary: 0 or 1. There are two types of measurables in regression. There’s an explanatory variable, or item being measured, and the response variable, which is the resulting outcome.
There are three basic kinds of logistic regression. The first is binary logistic regression, where there are only two possible outcomes for a categorical response. Think of this from the sense of a yes/no or pass/fail situation. Meanwhile, multinomial logistic regression includes three or more response variables. An example of this is predicting what a diner will order at a restaurant from certain options on the menu. Finally, there’s ordinal logistic regression. While this does include three or more variables like multinomial regression, there’s an order the measurements must follow. An example of this would be providing a Yelp review with a 1- to 5-star rating.
Assumptions of Regression
When working with logistic regression models, there are certain assumptions that need to be made by statisticians and technicians. In binary logistic regression, it’s necessary that the response variable is binary. Again, this is a yes/no or pass/fail situation, a 50-50 if you will. The desired outcome of a regression model should also be represented by the factor level 1 of the response variable; the undesired is 0. It’s also important that these models and algorithms include variables that are meaningful to the structure.
Within logistic regression, independent variables have to essentially be independent of one another. There should be minimal or no multi-co-linearity. Log odds and independent variables have to be linearly related. It’s also important to remember that logistic regression is intended for large sample sizes. LR models are almost ineffective if you’re pooling 10 people, as opposed to 10,000.
Logistic Regression Applications
Several fields of industry, especially in health and social sciences, have found great success in logistic regression. One of the common applications in the health care field is the Trauma and Injury Severity Score, or TRISS. TRISS is used to predict fatality in injured patients, developed with the application of LR to use variables such as the revised trauma score, injury severity score, and the age of a patient to predict health outcomes. These diagnostics also help medical care professionals to make approximations based on age group, health history, weight, and gender to predict the possibility of a person contracting an ailment.
In politics, pollsters use LR models to determine who a voter will cast their ballot for. These predictions can be done based on party registration or more personal statistics like age and gender. In the e-commerce and marketing industry, logistic regression models help retailers to understand their current customer base and what could attract more traffic to their websites. Having the right regression models in place can save businesses a lot of time and energy when worrying about the what-ifs.