Many data scientists mention regression techniques. Regression takes a significant chunk of your life when you become a professional data scientist. If you are on a data science track, paying attention to regression types and techniques won't harm you! Before considering a data science training program, it's better to run your eyes on the topic.
Despite being such a relevant topic, many learners find it challenging to get a stronghold of regression. And that’s when others move over them! So, you cannot overlook regression if you wish to launch a successful career in data science. But, where do you begin? If something similar has been bothering you, you have visited the best place.
This article gives you a deep dive into the popular regression types and techniques used in data science. So, you will become a professional in leveraging regression in data science projects and pass the data science certification after scanning through this post.
Meaning of regression
Regression analysis is a set of statistical techniques used in statistical modeling to estimate the associations between a dependent variable (commonly referred to as the 'outcome variable') and multiple independent variables.
Regression analysis is most commonly used for two goals. For starters, it's widely utilized for prediction and forecasting, which is a discipline that overlaps with machine learning. Second, it is implemented to infer causal links between independent and dependent variables.
Importance of regression in data science
Enterprises may utilize regression analysis to understand better their data points and how to use them intelligently in conjunction with other business analytical tools to make better decisions.
When one of the independent variables is modified while the other independent variables stay fixed, regression analysis can help an individual comprehend how the typical value of the dependent variable changes. As a result, Business Analysts and other data professionals use this robust statistical tool to eliminate unnecessary variables and select only the most important ones.
The advantage of regression analysis is that it helps organizations use data crunching to make better decisions. A better grasp of the variables can impact a company's success in the coming weeks, months, and years.
Regression types and techniques you should know
Although regression is a massive topic, you should know a few types to carry out data science projects effortlessly. Let's learn about these varieties in greater detail right below!
Linear regression is the most often used modeling technique, which assumes a linear association between a dependent variable like Y and an independent variable like X. A regression line, also referred to as a best-fit line, is used in linear regression.
Y = c+m*X + e signifies the linear connection, where 'c' denotes the intercept, 'm' indicates the line's slope, and 'e' represents the error term.
Simple (just one dependent and one independent variable) or sophisticated (many dependent and independent variables) linear regression models exist (with one dependent variable and more than one independent variable).
The logistic regression technique can be used when the dependent variable is discrete. In other words, this method calculates the likelihood of mutually exclusive events like pass/fail, true/false, 0/1, etc.
As a result, the target variable can only take one of two values, a sigmoid curve illustrates its relationship to the independent variable, and probability ranges from 0 to 1.
The polynomial regression analysis technique represents a nonlinear association between dependent and independent variables. The best fit line is not straight but curved in this form of the multiple linear regression model.
The ridge regression technique is used when data is multicollinear or when the independent variables are highly correlated. While least squares estimates are impartial toward multicollinearity, their variances are significant enough to cause observed and actual values to diverge.
By biasing the regression estimates, ridge regression minimizes standard errors.
The multicollinearity problem is solved by the lambda () variable in the ridge regression equation.
Like ridge regression, the Least Absolute Shrinkage and Selection Operator, often known as the Lasso approach, penalizes the absolute magnitude of the regression coefficient. In addition, the lasso regression technique uses variable selection, which causes coefficient values to drop to absolute zero.
Quantile regression is used in statistics and econometrics. The linear regression technique is a subset of the quantile regression strategy. It's used when the linear regression conditions aren't met or the data contains outliers.
- Bayesian Linear Regression
Bayesian linear regression is an ML regression analysis technique that employs Bayes' theorem to derive the values of regression coefficients. Rather than computing the least squares, this method estimates the posterior distribution of the features.
As a result, the technique surpasses traditional linear regression in terms of stability.
- Principal Components Regression
The principal components regression technique is frequently used to evaluate multicollinear regression data. By biasing the regression estimates, the significant components regression approach, like ridge regression, minimizes standard errors.
The training data is first modified using principal component analysis (PCA), and then the transformed samples are utilized for training the regressors.
- Partial Least Squares Regression
The partial least squares regression approach is a covariance-based regression analysis technique that is quick and efficient. It is helpful for regression issues with many independent variables and a high likelihood of multicollinearity between the variables.
The approach works by reducing the number of variables to a reasonable number of predictors, which can be used in the regression.
Elastic net regression assimilates ridge and lasso regression techniques, especially useful when dealing with highly linked data. It uses the penalties associated with the ridge and lasso regression methods to regularize regression models.
The final words:
You've grasped the basics of regression in data science! It's better to deep dive into these techniques and gets good practice using them in real-life projects. You can expect several questions about regression in any data scientist interview. Since your foundation is well-laid, you can bag any job.
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