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The term regression refers to the estimation or forecast of one variable's average value for a given value of another variable. And regression analysis is a statistical technique for determining the relationship between a dependent and independent variable.

Linear regression and logistic regression are two types of regression analysis techniques utilised to tackle the regression problem using machine learning. They are the most often used regression approaches. However, there are many different types of regression analysis approaches in machine learning, and their use varies depending on the data.

In this article, we'll go through the various forms of regression in machine learning and when each of them can be employed. If you're new to machine learning, this article will undoubtedly assist you in grasping the notion of regression modelling.

What is Regression Analysis and How Does It Work?

In a dataset, regression analysis is a predictive modelling technique that examines the relationship between the target or dependent variable and the independent variable. When the target and independent variables have a linear or non-linear connection and the target variable has continuous values, numerous types of regression analysis techniques are applied. The regression technique is primarily used to identify predictor strength, forecast trend, time series, and cause and effect relationships.

In machine learning, regression analysis is the most common technique for resolving regression problems using data modelling. It entails establishing the best fit line, which is a line that passes through all of the data points with the least amount of distance between them.

The different types of regression in machine learning techniques are explained below in detail:

1. Linear Regression

Linear regression is a basic regression type in machine learning. It links predictor and dependent variables linearly. Multiple linear regression models are used when there are several independent variables in the data. Linear Regression Models can be divided into two types:-

● A linear regression model with one independent and one dependent variable is known as simple linear regression. ● A linear regression model with more than one independent variable and one dependent variable is known as multiple linear regression.

2. Logistic Regression

When the dependent variable is discrete, logistic regression is used. For example, 0 or 1, true or false. A sigmoid curve represents the connection between the target variable and the independent variable.

The Logit function is used in Logistic Regression to connect the target and independent variables.

3. Ridge Regression

This is another sort of machine learning regression that is utilised when the independent variables are highly correlated. Because least squares estimations yield unbiased values for multicollinear data. However, strong collinearity can lead to a bias value. So a bias matrix is added to the Ridge Regression equation. This is a powerful regression approach that avoids overfitting.

4. Lasso Regression

Lasso Regression is a type of machine learning regression that performs regularisation and feature selection. It limits the regression coefficient's absolute size. As a result, the coefficient value approaches zero, unlike in Ridge Regression.

Thus, in Lasso Regression, a set of features from the dataset is selected to form the model. In Lasso Regression, only required characteristics are used, while the rest are zeroed. This prevents overfitting in the model. When the independent variables are substantially collinear, Lasso regression picks one and reduces the others to zero.

5. Polynomial Regression

Polynomial Regression is a machine learning regression technique that is similar to Multiple Linear Regression with a few modifications. The n-th degree relationship between independent and dependent variables, X and Y, is used in Polynomial Regression.

It's an estimator using a line. Polynomial Regression uses Least Mean Squared Method. The best fit line in Polynomial Regression is curved and depends on the power of X or the magnitude of n.

6. Bayesian Linear Regression

Bayesian Regression is a sort of machine learning regression that uses the Bayes theorem to calculate regression coefficients. Instead of calculating the least-squares, this regression finds the posterior distribution of the features. Bayesian Linear Regression is similar to Linear Regression and Ridge Regression but more stable.

7. Quantile Regression

Quantile Regression is used when the prerequisites for using Linear Regression are not met. It is an extension of linear regression analysis and can be used when there are outliers in the data.

8. Elastic Net Regression

When dealing with highly correlated variables, elastic net regression is preferred over ridge and lasso regression.

9. Principle components regression

Principle components regression technique which is broadly used when one has various independent variables. The technique is used for assuming the unknown regression coefficient in a standard linear regression model. The technique is divided into two steps,

1. Obtaining the principal components 2. Go through the regression Analysis on Principle components.

10. Partial least regression

It is an alternative for principal component regression when there is a high degree of correlation. It helps when there are several independent factors. Applied in the chemical, pharmaceutical, food, and plastic industries.

11. Support Vector Regression

SVR can solve both linear and nonlinear models. Support vector regression has been proven to be successful in estimating real-value functions.

12. Ordinal Regression

Ordinal regression predicts ranking values. The method works well with ordinal dependent variables. Ordinal regression includes ordered logit and ordered probit.

13. Poisson Regression

Customer service calls for a certain product are predicted using Poisson Regression. It is used when the dependent variable is calculable. Poisson regression is used to model contingency tables. y has a Poisson distribution.

14. Negative Binomial Regression

Unlike Poisson regression, negative Binomial regression does not predict a count distribution with variance equal to the mean.

15. Quasi Poisson Regression

Negative Binomial Regression is replaced by Quasi Poisson. The method works for overdispersed count data.

16. Cox Regression

To gather time-to-event data, utilise Cox regression. It demonstrates the time influence of variables. Cox Regression is also known as proportional Hazards Regression.

17. Tobit Regression

Tobit regression is used to evaluate linear correlations between variables when the dependent variable has censoring (all observations). The dependent's value is given as a single.

Conclusion

Other machine learning regression approaches include Elastic Net Regression, JackKnife Regression, Stepwise Regression, and Ecological Regression.

These regression analysis techniques can be utilised to develop the model depending on the data provided or the most accurate. You may study more about these strategies or take our online supervised learning course.

A beginner’s Guide by Learnbay

Data science learning resources are flooding everywhere. Unfortunately, such massive availability of online courses, practice websites, competition sites, and e-books can easily puzzle you to choose the wrong one. So, to make your data science career transition mistake-proof from learning resources, I have developed five professional pieces of advice for you.

Let’s see what those are.

1. Choose a Course that offers Interactive Learning Mode and end-to-end career transition support.

Recorded sessions are the best option for brushed up learning. While you are learning from scratch, then the model of learning has to be interactive fast. Such a session usually is not available for free. You need to pay for that. So, before finalising a paid data science learning course, ensure if it provides complete career transition assistance or not. Assess for the following. ● Live online classes/ offline classes ● Domain-specific Hands-on industrial project experience ● Capstone project opportunity ● Job assistance with interview questions and mock interviews. ● Domain-specific Internship (added advantage) ● Life-long access for course modules and recording of classes.

2. Choose tools training resource that supports your domain expertise

Opting for an additional online resource for practising the knowledge of your learned data science tool is fine. But be assured you are ● Learning and Practising the right tool ● assessing the latest version of each tool ● Following guidelines and techniques for the tools are on-demand in your industrial domain. ● Learning the best-fit usage strategies currently available.

3. Keep your focus on learning modules.

To gain the maximum possible benefits of a learning resource, check if the associated learning modules are ● Focused on your domain expertise. ● Focused on your level of working experience. ● Highly competent with the current job market demand. ● Offer you the scopes of learning from scratch. ● Includes all the theoretical concepts, practical tools, and soft skill development opportunities for a sustainable data science career transition within your domain.

4. For projects, cross-check data resource reliability.

Finding a large amount of data set is quite easy but finding the right one is not easy. With the massive development of IoT, the exposure of data sets is now expanding, but that also expands the reliability concern. Before inserting data set in your project, ensure it's ● free from legal hazards ● Cross-verified ● Bug-free ● Up-to-date ● Authenticated ● Original Better to verify the datasets from multiple resource platforms.

[Related Blog: The Secrets of Completing Highly Creditable Hand-on Project in 2021]

5. For e-books, tutorial videos, technical blogs, - choose the updated one only.

Listening to blogs, streaming youtube videos, and reading e-books are the best strategy for on-the-go continued learning in our busy lifestyle. But stick to such resource only that are ● Regularly updated ● Updated for tech tool’s new features, bug fixations, etc. ● Republished or launched with latest conceptual and informative updates (for the book/ blogs)

[Related blog: Top 7 Books Every Data Science Aspirants Should Read in 2021| Learnbay]

Mater Advice

Whatever resource you choose, it should always provide you with an in-depth idea, not the cheatsheet one. Besides, the resource has to be acceptable enough to become a smart but knowledgeable data scientist rather than hardworking.

The best strategy is to choose such a resource that offers ample opportunities for dealing with practice problems.