What is Isotonic Regression and How Can a Business Utilize it to Analyze Data?

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What is Isotonic Regression?

Isotonic Regression is a variant of linear regression and allows us to build models in piecewise linear manner i.e., breaking up the problem into few or many linear segments and performing linear interpolation of each function.

This analytical technique is designed to explore the relationship between two or more variables (X, and Y). It is useful in identifying important factors (Xᵢ) that will impact a dependent variable (Y), and the nature of the relationship between each of the factors and the dependent variable.

Isotonic Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature.The minimum sample size is 20 cases per independent variable.

In order to get a comprehensive understanding of Isotonic Regression, let’s look at a sample analysis to determine a student’s chance of admission based upon various academic scores.

If we consider the use cases below, we can see the value of Isotonic Regression.

Use Case – 1

Business Problem: Decide Loan Eligibility based on Applicant’s Annual income, Employment Period, Debt to Income Ratio etc.

Input Data: Predictor/Independent Variable(s) to determine Applicant’s Loan Eligibility:

• House Ownership Status
• Employment Length
• Annual Income
• Loan Verification Status
• Debt to Income Ratio

Loan applicant’s can discover what predictors can lead towards the required loan amount to be eligible for further proceedings in turn ensuring systematic banking approach and also assist banks to check the loan eligibility criteria before sanctioning a loan to the applicant.

Use Case – 2

Business Problem: Predicting diamond prices using basic measurement metrics.

Input Data: Predictor/Independent Variable(s) to determine the price of a Diamond:

• Carat weight of Diamond
• Quality of the Cut
• Diamond Color
• Clarity
• Depth
• The width of the diamond’s table