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Understanding Logistic Regression using R

 

1. What is Logistic Regression?

Logistic Regression is one of the machine learning algorithms used for solving classification problems. It is used to estimate probability whether an instance belongs to a class or not. If the estimated probability is greater than threshold, then the model predicts that the instance belongs to that class, or else it predicts that it does not belong to the class as shown in fig 1. This makes it a binary classifier. Logistic regression is used where the value of the dependent variable is 0/1, true/false or yes/no.


Example 1

Suppose we are interested to know whether a candidate will pass the entrance exam. The result of the candidate depends upon his attendance in the class, teacher-student ratio, knowledge of the teacher and interest of the student in the subject are all independent variables and result is dependent variable. The value of the result will be yes or no. So, it is a binary classification problem.


Practical Implementation of Logistic Regression

Problem Statement: - In this problem, we want to predict if a person is suffering from heart disease or not.

Data Description:- To solve this problem, I have downloaded a heart disease dataset from Kaggle (https://www.kaggle.com/ronitf/heart-disease-uci) to predict if a person is having heart disease or not.



Comments

  1. AnonymousJanuary 25, 2022 at 3:40 AM

    Simple Linear Regression in Machine Learning (SLR) is a tactic that can help to review and evaluate relationships between two factors.
    I would like to thank you for the efforts you have made in writing this article.
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