In the oeuvre of machine learning algorithms, Support Vector Machines stand out for their efficiency, robustness, and effectiveness in coping with complex tasks of classifications. The method was introduced by Vladimir Vapnik and his colleagues in the early 1990s and has become a cornerstone in the landscape of supervised learning, making significant advances in both practical applications and theory.
In this blog, we will learn about the machine learning basics focusing on the SVM algorithm and how it works.
What Is Support Vector Machines?
SVM or Support Vector Machines represents a class of supervised learning algorithms that are typically used for regression and classification tasks. The primary use of the SVM algorithm is for solving problems in classification, and in this context, its main focus is on finding a hyperplane or an optimal decision boundary that proves effective for separating data points that belong to different classes.
How Does Support Vector Machines Work?
SVM works by looking for an optimal hyperplane in a high-dimensional feature space that efficiently separates data points that belong to various classes. Let’s look at a step-by-step explanation of how SVM machine learning works.
Preparation of Data
One starts with a labeled set of data, which means a class label is associated with each data point which is in turn represented as feature vectors where every feature represents a different property of the data.
Feature Scaling
In order to make sure that features are on the same scale, it would be advantageous to opt for feature scaling. This step involves the performance and convergence of the SVM algorithm.
Linear Separation
The aim of SVM is to find the hyperplane that linearly differentiates the data points into different classes.
Maximising Margins
SVM aims to maximize the margin, which is the distance between the nearest data point and the hyperplane (called support vectors). By maximizing this margin, SVM enhances the ability of generalization and the classifier’s robustness.
Finding The Optimal Hyperplane
It is through solving an optimization problem that the optimal hyperplane is found. SVM tries to minimize errors in classification while maximizing the margin.
Dealing with Non-Linear Data
Source: Unsplash
When data can’t be linearly separable, SVM can be extended with the use of a ‘kernel kick.’ In place of explicitly mapping the data into a higher-dimensional space, the kernel functions help in computing the dot product of feature vectors in the higher-dimensional space without actually computing the mapping. This makes it easy for SVM to find a non-linear separating hyperplane effectively.
Regularization
For effective handling of overlapping classes or noisy data, SVM introduces a regularized parameter C which controls the trade-off between allowing some misclassifications and maximizing the margin.
Classification
Once the optimal hyperplane is found, SVM can easily classify unseen and new data points on the basis of the side of the plane they will fall on.
Handling With Multi-class Classification
Although SVM is primarily a binary classifier, there exist several techniques, such as One-vs-All or One-vs-One, for extending SVM for completing multi-class classification tasks.
Tuning And Model Evaluating
At last, the valuation of the performance of the SVM model can be done with the use of appropriate metrics like recall, accuracy, F1-score, and precision. One can also fine-tune the parameters like the C value, and choice of kernel, among others, for optimization of the performance of the model.
Conclusion
SVM algorithms are quite effective and versatile for different classification tasks, and it is by understanding all these steps that you can build and apply SVM models to databases in the real world.
However, it must also be noted that they come with some tuning and computational challenges, which makes them less ideal for situations where interpretability is crucial or for very large databases. You must ensure a detailed understanding of the specific data characteristics and the trade-offs to decide whether SVM is the perfect choice for a given problem in machine learning.
