What is Regression ? Know about it’s types

• In this article you will learn:
• 1.Introduction.
• 2.What is Regression Analysis?
• 3.What is the purpose of a regression model?
• 4.Types of Regression Models Analysis / Different Regression Models.
• 5.Conclusion.

Introduction:

Regression problems are common in machine learning and regression analysis is the most commonly used technique for dealing with them. It is based on data modelling and involves determining the best fit line that passes through all of the data points with the shortest distance between the line and each data point. While there are other regression analysis techniques linear and logistic regression are the most commonly used. Finally the nature of the data will determine the type of regression analysis model that can be used.

What is Regression Analysis?

To determine the relationship between a dataset’s dependent (goal) and independent variables, predictive modelling techniques such as regression analysis can be used. When a dependent and independent variable are linked in a linear or non-linear fashion and the target variable has a set of continuous values it is widely used. Thus regression analysis approaches aid in the establishment of causal relationships between variables time series modelling and forecasting. For example regression analysis is the best way to investigate a corporation’s relationship between sales and advertising expenditures.

What is the purpose of a regression model?

Regression analysis can be used to do one of two things predict the value of the dependent variable when you know something about the independent variables or predict how the independent variables will affect the dependent variable.

Types of Regression Analysis:

There are many different ways to use regression analysis to make predictions. Also the choice of a method depends on a number of factors such as the number of independent variables the shape of the regression line and the type of the dependent variable.Let us examine several of tmost often utilized a regression analysis techniques:

1. Linear Regression:

A linear regression is the most common modelling method. It assumes that the relationship between the dependent variable (Y) and an independent variable (X) is linear (X). It uses regression line which is also called “best-fit line.” The linear relationship is shown by the equation Y = c+m*X + e.The linear regression model can be simple (with only one dependent and one independent variable) or complex (with many dependent and independent variables).

2. Logistic Regression:

When a dependent variable is discrete a logistic regression technique is applicable. This method is used to figure out the chances of events that can’t happen together, like pass/fail, true/false, 0/1 and so on. So a target variable can only have one of two possible values and a sigmoid curve shows how it is related to an independent variable. Probability has a value between 0 and 1 which is between 0 and 1.

3. Polynomial Regression:

A polynomial regression analysis is used to show that the relationship between the dependent variable and the independent variable is not linear. It’s like a multiple linear regression model but the best fit line is curved instead of straight.

4. Ridge Regression:

When data has more than one kind of correlation which is called “multicollinearity” the ridge regression technique is used. Even though estimates based on least squares are fair in multicollinearity their differences can be big enough to cause an observed value to be different from the real value. Ridge regression lowers standard errors by making regression estimates more biassed.Multicollinearity is solved by the lambda () variable in a ridge regression equation.

5. Lasso Regression:

The lasso (Least Absolute Shrinkage and also Selection Operator) technique like the ridge regression penalises the absolute magnitude of the regression coefficient. Furthermore the lasso regression technique employs variable selection which results in the shrinkage of coefficient values to absolute zero.

6. Quantile Regression:

A quantile regression technique is a subset of a linear regression technique. It is used when the requirements for linear regression are not met or when the data contains outliers. Quantile regression is used in statistics and econometrics.

7. Bayesian Linear Regression:

Bayesian linear regression is a regression analysis technique used in machine learning that employs Bayes’ theorem to compute the values of the regression coefficients. Rather than determining the least-squares this technique determines the posterior distribution of a feature. As a result in terms of stability the approach outperforms standard linear regression.

8. Principal Components Regression:

A principle components regression approach is frequently used to evaluate multicollinear regression data. Like aridge regression the significant components regression approach reduces standard errors by biassing regression estimates. Principal component analysis (PCA) is used to modify training data before using the transformed samples to train regressors.

9. Partial Least Squares Regression:

The partial least squares regression technique is a covariance-based regression analysis technique that is quick and efficient. It is useful for regression problems with many independent variables and a high likelihood of multicollinearity between the variables. The method reduces the number of predictors to a manageable number which is then used in regression.

10. Elastic Net Regression:

Elastic net regression combines the ridge and lasso regression techniques, which come in handy when dealing with highly correlated data. It uses penalties associated with the ridge and lasso regression methods to regularise regression models.

Conclusion:

It denotes a relationship between two variables one dependent and one independent and it shows the magnitude of an independent variable’s effect on the dependent variable. It necessitates a thorough understanding of statistical tools and applications. The correct method was chosen based on the variable’s, data’s and model’s nature. Overall various types of Regression Analysis have made it easier to calculate discrete and distinct data in recent years not only in the field of mathematics/statistics but also in many real-world applications. As a result regression analysis is a boon to humanity.