Regression in Machine Learning

Machine Learning is the most recent evolution in the world of technology and it has started showing its impact in the industry already. Creating a significant demand for jobs as well as skilled professionals with the desirable knowledge of ML techniques, the technology is fast growing and evolving by the hour.

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One such technique is regression. This blog delves deeper into the concept of regression in machine learning, a popular concept taught in most courses teaching ML skills to students.

Meaning of Regression Analysis

Regression analysis refers to a predictive modeling technique that is used to evaluate the relation between the dependent and independent variables in a set of data.

There are different types of regression analysis techniques. These techniques are used when a linear or non-linear relationship is represented between the dependent and independent variables and the dependent variable consists of continuous values. The regression technique then comes into play to assess the forecast trend, time series, and the strength of the predictor.

Among the many techniques used to solve regression problems in machine learning, regression analysis is the most often used involving data modeling. It helps ascertain the most suitable line, one which crosses all data points in a manner that the distance of the line from each data point is the minimum.

Let us cite an example to better understand the regression model in analyzing data using the linear regression machine learning technique. It can be used to foresee the sales of a company in the future based on previous sales data and advertising spend. For instance, regression analysis may indicate an increase in sales by Rs 5,000 for every Rs 1,000 spent on advertising activities for a specific product.

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Types of Regression Analysis Techniques

As stated earlier, there are different types of machine learning regression models used and the use of each model is dependent on various factors. These include the shape of the regression line, the type of dependent variable, and the total number of independent variables. Here are some of the different types of regression analysis techniques:

Ridge Regression - Used in case of a high correlation between independent variables

• Linear Regression - One of the most basic types of regression techniques comprising a dependent variable and a predictor variable linearly related to one another.
• Bayesian Linear Regression - Uses the Bayes theorem to determine the value of regression coefficients.
• Logistic Regression - Used when the dependent variable is discrete - true or false, 0 or 1, etc.
• Polynomial Regression - The relationship between dependent and independent variables (X and Y) is denoted by the n-th degree.
• Lasso Regression - When the independent variables are highly collinear, this technique picks only one variable and makes the others shrink to 0.

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Benefits of Regression Analysis

Here are some of the top advantages of using the regression technique in machine learning:

• Evaluating models - Regression analysis equips you with the necessary tools, such as adjusted R-squared, R-squared, and root mean square error (RMSE), which help evaluate the correct fit of the model. These metrics are used to assess the accuracy and credibility of the regression model, which in turn helps make informed decisions.
• Quantify relationships - Regression analysis also enables ML researchers to quantify the relationship between a dependent variable with one or more independent variables. Further, it helps determine the direction and strength of these relationships by providing numerical coefficients. To cite an example, the regression analysis technique can be utilized in a study to ascertain the relation between study hours and exam scores, i.e., the proportional change observed in exam scores with every additional study hour.
• Decision-making - Regression analysis can help provide useful insights into data that in turn helps researchers make informed decisions. It can be effectively used to determine resource allocation, design marketing strategies based on consumer behavior, or evaluate the potential impact of policy changes.

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Summing Up

Regression analysis is a useful technique in Machine Learning and there are many more regression machine learning models, as mentioned in this blog. These techniques are particularly helpful in building the model based on the type of data that is the most accurate or available.

Amity Online allows students to gain deeper insights into the concept of Machine Learning as well as its techniques, such as Regression Analysis. The platform offers online BCA and MCA courses specifically tailored for students and working professionals to learn advanced concepts in Machine Learning, both by way of theory and practical learning. You will also receive complete assistance in internships and placement support from the university after completing the learning program.

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