Binomial Logistic Regression

Binomial Logistic Regression

Binomial Logistic Regression

Description

Binomial Logistic Regression predicts the probability that an observation falls into one of the two categories of the binary dependent variable, based on one or more, categorical or continuous independent variables.

Why to use

Data Classification

When to use

In supervised learning, when you want to predict the probability of an observation belonging to one of the two categories of the binary dependent variable.

When not to use

It should not be used when the dependent variable is continuous.

Prerequisites

It should be used only when the dependent variable is binary.

Input

Any dataset that contains binary, categorical and continuous data, where the dependent variable is binary.

Output

Regression analysis characteristics in the form of Key Performance Index, Confusion Matrix, ROC Chart, Lift Chart, and a Coefficient Summary

Related algorithms

  • Multinomial Logistic Regression

Alternative algorithm

-


Statistical Methods used

  • Sensitivity
  • Specificity
  • F Score
  • Accuracy
  • Confusion Matrix
  • ROC Chart
  • Lift Chart
  • Coefficient
  • Standard Error
  • t test
  • p Value

Limitations

Works satisfactorily for binary classification, but does not give reliable results if used for regression problem.


In Binomial Logistic Regression algorithm, we check whether the observation falls into one of the two categories of the binary dependent variable (like 1/0, True/False, Yes/No, and so on) for a given set of continuous independent variables. Even though the algorithm name contains the word regression, it is not used for continuous variables.