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World of Regression

The relationships among variables. Used for predictive modeling and data mining tasks.

The word “Regression” came into existence due to “Sir Francis Galton”. There are more than 10 types of regression algorithms designed for various types of analysis. Each type has its own significance. Every analyst must know which form of regression to use depending on type of data and distribution.

Lets take a simple example : We are a small company that sell milktea. There can be a hundred of factors that affects our sales. In our case, sales is your dependent variable. Factors affecting sales are independent variables.

It helps us to answer the following questions:

  • Which of the factors have a significant impact on sales
  • Which is the most important factors for sales
  • How do the factors interact with each other
  • What would be the annual sales ot the next year

Type of Regression:

1. Linear Regression

2. Polynomial Regression

3. Logistic Regression

4. Quantile Regression

5. Ridge Regression

6. Lasso Regression

7. ElasticNet Regression

8. Principal Component Regression

9. Partial Least Square Regression

10. Support Vector Regression

11. Ordinal Regression

12. Poisson Regression

13. Negative Binomial Regression

14. Quasi-Poisson Regression

15. Cox Regression

Change log update

  • 29.01.2019


License

MIT

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