Definition:
Logistic regression is also a prediction-based analysis. If the dependent variable of your data set is binary, then logistic regression perfectly suits it. Logistic regression can define as “the representation of a data set and the association between one dependent variable (whose type is binary) and one or many independent variables that can be of any type. In some situations, it may prove difficult to understand logistic regression analysis. Statistics permit the use of multiple tools for logistic regression analysis. According to mathematics, there must be only two possible choices: hired/ fired, yes / no accepted/rejected, etc. These two choices can be represented as “0” and “1”.Also, check out this Data Scientist Course in Hyderabad to start a career in Data Science.
Pros and Cons:
Logistic regression, no doubt, has proved itself more reliable than any other method of classification. The following are some of the pros and cons of the logistic regression method.
It works more smartly and quickly than other classification techniques.
It also proves very simple in dealing with complicated relationships as the other classification methods.
When the linear regression method is unable to solve the problem, so logistic regression method is applied to such datasets.
It deals with non-linear datasets and represents the relationship between them.
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Applications:
Nowadays, different fields of data science, such as machine learning, banks, and medical fields, are using the logistic regression method of analysis for predicting their data.
In the medical field Trauma and Injury Severity Score (TISS) is developed for the prediction of death in injured patients.
This method can also be used to predict the risk of the growth of any disease such as heart or brain etc., based on patients’ examined properties.
In natural language processing, this method is also applied.
The engineers are using this method for predicting whether the system or project being developed will result in a pass or fail.
This prediction method is also used in marketing, such as predicting the choice of a customer, e.g., whether to purchase or terminate any payment.
The economists also used this method of regression for predicting a person’s choice of yes/no based on particular conditions.
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Comparative analysis of logistic regression method with other methodologies:
This method shows the relationship between two variables (one of them is dependent that consists of choice and can be known as a binary variable because it includes two categories, and the second is independent that can be one or more than one). The relationship is measured using a logistic formula, a cumulative distribution function of the logistic distribution. So, this method deals with the same problems as Probit Regression Method does. The difference between these two is that the first uses the logistic method of distribution of errors, and the second uses a typical standard for distribution. Looking forward to becoming a Data Scientist? Check out the Data Science Certification and get certified today.
This method can also be utilized as the substitute for Fisher’s 1936 process called linear discriminant analysis. The logistic regression can be produced by reversing the condition that holds the assumptions of linear discriminant analysis. It does not mean that converse always proves itself true. It’s just because logistic regression does not need multivariate average assumptions of the linear discriminant analysis.
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