Understanding Support Vector Regression from Scratch

 In the world of machine learning, there are many approaches to prediction tasks. 

Among these, Support Vector Regression (SVR) is a powerful tool that applies the concepts of Support Vector Machines (SVM) to regression problems, allowing it to flexibly capture non-linear relationships.

This article explains the basic concepts of Support Vector Regression, the situations in which it is used, and the benefits of learning SVR.

1. What is Support Vector Regression?

Support Vector Regression is a regression technique that uses the SVM framework to predict continuous values.

- Basic Idea

First, deriving from the mechanism by which SVM finds the “optimal hyperplane” in binary classification problems, SVR introduces a certain tolerance (ε) to prediction errors. It constructs a regression function that keeps data points within this range. By applying a penalty to data points outside the range, the model becomes a “smoother” function, avoiding excessive complexity and overfitting.

- Utilizing the Kernel Trick

To handle not only linear relationships but also non-linear dependencies, SVR uses kernel methods (such as Gaussian kernels or polynomial kernels) to map data into high-dimensional feature spaces. This allows it to beautifully capture complex patterns and be applied to diverse data from the real world.

This simple yet sophisticated mathematical foundation provides SVR with a robust and flexible regression model for making precise predictions.

2. In What Situations is it Used?

Support Vector Regression is used in a variety of practical fields due to its high flexibility and accuracy. Here are some representative examples:

- Economic Forecasting and Financial Markets

In forecasting stock prices in financial markets and analysing trends in exchange rates and economic indicators, SVR is often used to make more accurate predictions due to the high levels of noise and non-linear relationships present.

- Energy Demand Forecasting

SVR is used as a stable demand forecasting model to predict energy consumption, which is affected by complex factors such as weather, time, and regional characteristics, successfully capturing its non-linear patterns.

- Healthcare and Bioinformatics

SVR is gaining attention as a useful tool in clinical applications, such as predicting treatment effects and analysing disease risk, for complex and diverse healthcare data, including patient vital signs, test results, and genetic data.

- Meteorology and Environmental Modelling

Because environmental data such as temperature, humidity, and rainfall often exhibit non-linear variations, SVR is used as a powerful regression technique in weather forecasting and environmental simulation to capture subtle changes.

In these situations, the target data is often complex and multi-dimensional, making SVR's non-linear modelling capability highly effective and contributing to the construction of practical solutions.

3. What are the Benefits of Learning Support Vector Regression?

Learning SVR not only involves mastering regression analysis, but also offers many benefits that lead to a broad understanding of machine learning.

- Understanding Advanced Mathematics and Optimisation Theory

SVR is solved as an optimisation problem, providing a good opportunity to come into contact with mathematical backgrounds (such as the method of Lagrange multipliers and dual problems). This fosters the ability to deeply understand the internal structure of algorithms.

- Flexible Approach to Non-Linear Problems

Through the kernel method, you can handle complex non-linearities that cannot be handled by linear models, improving your ability to apply them to diverse problems in the real world. This will be a significant asset when tackling more complex problems in the future.

- Immediate Practical Application

SVR is implemented in libraries such as scikit-learn, making it easy to write code and evaluate it with actual datasets. This builds the skills to construct high-accuracy regression models in practical projects and improve the reliability of decision-making and predictions.

- A Bridge to Ensemble Learning and Other Algorithms

Learning SVR deepens your understanding of more advanced machine learning techniques (e.g., gradient boosting and neural networks). SVR is very useful as a foundational knowledge base when comparing and considering the characteristics and application conditions of each technique.

In Conclusion

Support Vector Regression (SVR) is a regression technique that flexibly and accurately captures non-linear patterns in data and is practically used in a wide range of fields, including finance, energy, healthcare, and meteorology.

For those of you who are considering venturing into the field of machine learning and data science, SVR is a valuable technology to learn from the basics to the application. We recommend that you first try writing code and experiencing SVR implementation and tuning, and feel its high accuracy and flexibility. This will surely deepen your data analysis skills.

If you want to learn Support Vector Regression, we recommend this book (access here).