About logistic regression

This is an article about logistic regression analysis.

hello!

Today we will learn about logistic regression analysis.

Logistic regression is a statistical analysis technique used when the dependent variable is binary (two categories) and is commonly used to deal with binary classification problems.

We will discuss logistic regression below.

Main concepts

Binary Classification

Logistic regression is primarily utilized when the dependent variable is binary, meaning it falls into one of two categories.

Logit Function

Logistic regression is utilized to predict the probability that a dependent variable falls into a specific category using the logit function.

Odds Ratio

Logistic regression describes the probability of a dependent variable through odds ratios, which determine the effect of independent variables on the dependent variable.

How it works

Logistic regression, unlike linear regression, uses the logit function to predict the probability of the dependent variable.

The logit function compresses the output of a linear equation into a range between 0 and 1, allowing it to be interpreted as probability.

This allows us to determine how changes in the independent variable affect the probability of the dependent variable.

uses

Logistic regression analysis is used in a variety of fields, including medicine, finance, marketing, biology, and social sciences, in the following fields:

Disease Diagnosis

It is used to predict whether a patient has a specific disease.

marketing

It is used to predict whether a customer will purchase a particular product.

finance

It is used to predict the likelihood of loan default.

caution

When using logistic regression, you must consider issues such as multicollinearity, outliers, and overfitting when building your model.

Conclusion

Logistic regression is effective in handling binary classification problems and is actively used in various fields that require probabilistic modeling.

thank you!