Logistic regression

Statistical models are integral to the growth and progress of artificial intelligence (AI), and logistic regression is no exception. Pairing AI and logistic regression delivers accurate predictions for many uses. Learn more about logistic regression, what it is used for and how it works with Micron.

What is logistic regression?

Logistic regression definition: Logistic regression is a statistical model, also called the logit model, designed to find a relationship between two data factors and predict outcomes.

What separates logistic regression from other statistical models used with artificial intelligence is that it provides a finite answer as its predicted outcome rather than a nuanced range of data. For example, a logistic regression model may result in a simple yes or no outcome.

It is also an increasingly popular model for machine learning because it offers simplicity and speed for businesses that need actionable data and predictions quickly.

How does logistic regression work?

Like other statistical models using artificial intelligence tools, such as linear regression, the logistic regression model has an equation or function at the root of its workings. The sigmoid function is key to the model's workings.

The sigmoid function helps the logistic regression model attribute and convert any real value from the dataset into a value between 0 and 1. This approach allows logistic regression to produce more factual answers such as yes, no, true or false. The most basic principle of logistic regression is its ability to accurately model the probability of binary outcomes.

What is the difference between linear and logistic regression?

Linear regression and logistic regression are two common statistical models that overlap, but there are some subtle differences. While both models are used to identify the relationship between a dependent and independent variable, linear regression works with continuous dependent variables whereas logistic regression works only with categorical variables.

What is the history of logistic regression?

Like linear regression, logistic regression has a long history.

• 1800s, early foundations: Logistic regression is rooted in mathematical principles that date back to the 19th century, when Pierre-Simon Laplace and Daniel Bernoulli, followed by Sir Francis Galton, researched probability and binary outcomes.
• 1940s-1950s, application to binary questions: In the mid-20th century, logistic function was applied to binary classification problems, which is when logistic regression as a machine learning model was developed. This application of mathematical theory to datasets opened the door to the development of machine learning models based on this statistical analysis.
• 1980s onward, modern applications: Since the late 20th century, logistic regression has been applied to classification problems. It has become a staple of machine learning, allowing the power of probability to be harnessed across industries.

What are key types of logistic regression?

The three main types of logistic regression have their own use and meaning for different situations:

Binary logistic regression is the most commonly used type of logistic regression, where the dependent variable in the dataset has only two possible outcomes. It is particularly useful when analyzing data with only two potential results. For example, binary logistic regression can determine whether a student is likely to pass a test or if a voter is likely to vote for a specific candidate based on prior performance.

Multinomial logistic regression has dependent variables with more than just the two possible outcomes that are found in binary logistic regression. These values are also achronological, meaning that they are in no specific order. This type of logistic regression is useful for estimating a person's job position based on external factors such as age, gender, and qualifications.

Ordinal Logistic Regression is similar to multinomial logistic regression but also deals with dependent variables that have more than two possible outcomes. However, the difference lies in the values being ordered in a defined sequence. Ordinal logistic regression is typically used for ranking or scaling, such as assessing the severity of a medical condition

How is logistic regression used?

Logistic regression allows businesses and medical professionals to make predictions and, in turn, decisions that can guide their success and enhance their diagnoses. For example, in the detection of medical issues, even a basic binary logistic regression can help analyze and provide simple answers about how critical certain medical conditions or illnesses are.

As with similar predictive models, logistic regression in any form has a proven use case in the financial sector. By investigating and analyzing historical data, this statistical model provides further guidance on the level of risk in loan or insurance applications. Its straightforward and binary outcomes can flag certain applications as higher risk.

Logistic regression has also become an invaluable tool in marketing. It can look at user behavior and understand if or why a user is more likely to click on an advertisement, or why one advertisement might stand out more than another to users. Logistic regression allows marketing teams to make sure they are making the right decisions.