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Support vector machines (SVMs) are algorithms used to help supervised machine learning models separate different categories of data by establishing clear boundaries between them. As an SVM classifier, it’s designed to create decision boundaries for accurate classification. It is one of the key techniques data scientists use to create AI and ML models with a wide range of practical applications, including image recognition, fraud detection and spam filtering.
SVMs excel at processing high-dimensional data, such as a brain scan containing millions of data points. They can also protect against over-fitting, where a model performs well making predictions using the data on which it was trained but fares poorly when encountering new data.
This guide will describe how SVMs work and why they’re essential tools for ensuring accurate predictions using ML models.
SVMs operate by identifying where the margin between different categories of data is the greatest. For example, with an ML model trained using images of fruit, an SVM might learn to separate apples and oranges based on features like color, shape and texture, creating a boundary known as the hyperplane, which the model uses to distinguish between the two categories.
An SVM can work with both linearly separable and nonlinear data. With linear data, you could plot apples and oranges on a graph using features like weight and shape. Heavier, rounder objects (oranges) would cluster in one area, while lighter, less round objects (apples) cluster elsewhere. An SVM would find the optimal straight line that separates these clusters with the widest possible margin, then use this boundary (the hyperplane) to classify future images of fruit.
Classifying and separating non-linear data requires an extra step. Say you own a pizza restaurant and you want to identify where your most loyal customers live. You discover that your best customers tend to live near the restaurant, with less frequent visitors forming a ring around it at various distances. A graph of this data would look like a doughnut, with loyal customers forming the hole in the middle. But because the data isn’t linear, there’s no way to draw a line that clearly separates both groups. So SVMs rely on mathematical functions (called kernels) — a process known as the kernel trick — that can transform the data into multiple dimensions, making it possible to identify the largest boundary between the two groups. One of the most widely used is the Radial Basis Function (RBF) kernel, ideal for complex non-linear data.
Every SVM involves the following elements:
This is the decision boundary that separates different categories of data: a line in 2D spaces, a plane in 3D or a higher-dimensional surface in more complex data spaces. The SVM finds the optimal hyperplane that best divides the categories.
The data points that lie closest to the hyperplane and directly influence where the boundary is drawn are known as support vectors. These are the critical examples that actually define the decision boundary; if you removed them, the hyperplane would shift.
The margin is the distance between the hyperplane and the nearest data points from each class. SVMs maximize this margin to create the most robust separation possible between categories.
These mathematical functions transform data into higher dimensions to make linear separation possible. They allow SVMs to handle non-linear data by finding curved boundaries in the original space.
This value, typically expressed as C, controls the trade-off between maximizing the margin and minimizing classification errors. A high C value prioritizes correct classification over a wide margin, while a low C value trades classification accuracy in exchange for a wider margin.
Gamma values control how tightly the decision boundary adheres to the training data. High gamma creates very specific boundaries that closely follow individual data points, while low gamma creates smoother, generalized boundaries that ignore small details.
In cases where perfect separation of data points isn’t possible, slack variables allow some data points to be on the wrong side of the margin or hyperplane. Permitting small amounts of misclassification makes it easier to deal with noisy or overlapping data.
Here are the five essential steps each SVM takes when processing data:
The SVM takes the original input data and uses kernel functions to transform it into a higher-dimensional space where linear separation becomes possible. This step is crucial for handling non-linear data; what appears as a curved boundary in the original space becomes a straight line in the transformed space.
The SVM identifies the hyperplane (decision boundary) that creates the widest possible margin between different classes. It focuses on the support vectors — the data points closest to the boundary — and positions the hyperplane to maximize the distance to these critical points from each class.
When perfect separation isn't possible due to overlapping classes or noisy data, the SVM introduces slack variables that allow some misclassification. The regularization parameter (C) balances between maximizing the margin and minimizing these classification errors.
The SVM fine-tunes key hyperparameters like C, gamma and kernel choice to make the model complex enough to be accurate but simple enough to work well on new data it hasn't seen before. This helps the SVM avoid over-fitting, where a model becomes too specialized on training data and performs poorly on new examples.
For new, unseen data points, the trained SVM applies the same kernel transformation and simply checks which side of the learned hyperplane each point falls on. The distance from the hyperplane can also indicate the confidence level of the classification.
There are five primary types of support vector machines:
Linear SVMs are used when data can be separated with a straight line or a flat hyperplane. Because they do not rely on kernel transformations, linear SVMs are computationally efficient and easy to interpret.
When linear separation is not possible, non-linear SVMs employ kernel functions to transform data into higher dimensions. This creates curved decision boundaries in the original space, making it ideal for complex, non-linear data patterns such as a classic doughnut-shaped data set.
Designed for anomaly detection and novelty detection, one-class SVMs learn the boundary around "normal" data and identify anything outside it as an outlier or anomaly. This type of machine is typically used in fraud detection and quality control applications.
This type of machine uses SVM techniques to predict numbers instead of categories. Rather than drawing a line that separates different groups, SVR draws a line that best fits through the data points with some wiggle room for errors. SVRs are used for predicting things like prices, temperatures or sales figures.
Multi-class machines handle classification problems involving more than two categories by combining multiple regular SVMs. Since standard SVMs can only separate two groups at a time, this approach uses several SVMs working together to distinguish among multiple categories — for example, classifying images of fruit into apples, oranges and bananas.
SVMs are used in a broad range of applications where machine learning is employed. Some of the most common use cases include:
With their ability to quickly analyze pixel patterns and separate visual features, SVM classifiers excel at recognizing objects, faces and scenes in digital images. They're widely used in medical imaging to detect tumors in X-rays or MRIs and in security systems for facial recognition and surveillance.
SVMs analyze word patterns and linguistic features to automatically sort emails, documents and web content into categories. Email providers use them to filter spam by learning to distinguish between legitimate messages and unwanted promotional or malicious content.
By analyzing complex biological data patterns, these systems help classify DNA sequences, predict protein structures and identify disease-related genetic markers. They're particularly valuable in cancer research for classifying tumor types based on gene expression profiles.
SVMs convert handwritten text into digital format by analyzing stroke patterns, character shapes and spatial relationships in scanned documents. They're used by postal services to automatically read addresses on envelopes and in banking to process handwritten checks and forms.
These SVM algorithms analyze spending patterns, transaction amounts, locations and timing to flag potentially fraudulent financial transactions. Credit card companies and banks use them to detect anomalies in real-time and protect customers from unauthorized purchases.
In addition, SVMs are widely used in drug discovery to predict molecular behavior and identify promising pharmaceutical compounds. They also power recommendation systems for streaming services and ecommerce platforms by analyzing user preferences and behavior patterns to suggest relevant content or products.
SVMs are not appropriate for every machine learning use case. Here are the key advantages and limitations of deploying these algorithms.
They’re highly accurate. SVMs consistently deliver excellent classification performance across diverse data sets. By focusing on the most challenging data points (support vectors) and creating the widest possible separation between classes, they build robust decision boundaries that generalize well to new, unseen data.
They work well in high-dimensional spaces. SVMs handle data with many features (like analyzing thousands of genes at once) better than most other methods. While other algorithms get confused by too much information, SVMs actually get better because they only focus on finding the best boundary line instead of trying to understand every detail of the data.
They’re effective with small data sets. SVMs can build reliable models even when training data is limited, making them ideal for specialized domains like medical diagnoses or rare event detection. Their mathematical foundation allows them to extract maximum information from minimal examples, avoiding the over-fitting problems that plague other algorithms when data is scarce.
They’re memory efficient. SVMs only store the support vectors (the critical data points near the decision boundary) rather than the entire training data set. This makes them computationally efficient for making predictions and reduces storage requirements, especially valuable in applications with limited computational resources.
They’re versatile. SVMs can handle both simple straight-line problems and complex curved patterns by just switching the mathematical function (kernel) they use. This means you can tackle completely different types of data problems with the same basic SVM approach, just by picking the right kernel for your specific situation.
They can be computationally intensive. Because SVM training time increases dramatically with the number of data points, SVMs can become extremely slow and memory-hungry when dealing with massive data sets. Processing millions of examples can take hours or days, making them impractical for big data applications where faster algorithms are preferred.
They’re sensitive to kernel choice. Selecting the wrong kernel function can severely hurt SVM performance, and there's no universal rule for making the best choice. Different kernels work better for different data patterns, requiring extensive experimentation and domain expertise to find the optimal configuration for each problem.
They’re less effective when classes overlap. Because they're designed to find clear separation boundaries, SVMs struggle when different categories are heavily mixed together. When data points from different classes are scattered throughout the same regions, SVMs may create overly complex boundaries that don't generalize well to new data.
Their probability output is limited. Unlike some other algorithms, SVMs don't automatically provide probability estimates or confidence levels for their predictions. While probability estimates can be added, this requires extra computational steps and may not be as reliable as methods that inherently produce these outputs.
They can be difficult to interpret. SVM-based models can suffer from the “black box” problem, making it difficult to understand why they made specific predictions. This lack of interpretability can be problematic in fields like medicine or finance, where understanding the reasoning behind predictions is crucial for trust and regulatory compliance.
Here are five best practices for using SVMs:
SVMs can get confused when some data features are orders of magnitude larger than others. Make sure all your data features use similar number ranges (for example, converting both age and income to the same numerical scale) to prevent data on a different scale from skewing the results.
Each kernel captures different types of data patterns, so testing multiple kernel options helps find the best fit for your specific problem. You might want to start with a linear kernel for high-dimensional data, then try RBF for non-linear patterns. Consider polynomial kernels for structured relationships.
Test different combinations of settings (like C and gamma values) using a systematic approach that tries your model on multiple data subsets. This helps you find the best settings that will work well on new data, not just the data you used for training.
Keep a separate validation set to track how your model performs on unseen data during training and parameter tuning. If training accuracy is much higher than validation accuracy, reduce model complexity by lowering C or gamma values.
When one class of data is significantly larger than another (for example, 50 spam emails vs. 1,000 legitimate messages), adjust the SVM settings using class weights or sampling techniques to pay equal attention to both groups. Most SVM software can automatically balance this for you so the algorithm doesn't become biased toward the most common category.
Support vector machines are one of the most reliable machine learning algorithms due to their ability to create robust decision boundaries between different classes of data. They are particularly valuable both when working with limited training data and where precision is critical.
SVMs excel across numerous applications, including medical diagnosis, financial fraud detection, gene classification, spam filtering and handwriting recognition systems. Their ability to handle high-dimensional data makes them particularly suited for modern challenges like analyzing genetic sequences with thousands of features or processing text documents with extensive vocabularies.
They continue to be a powerful tool in both academic research and industry, especially for tasks requiring high accuracy and robust decision boundaries.
SVMs work best for sorting data into categories when you need very accurate results but don't have a large volume of training examples. They're especially good at handling complex data with many features, like analyzing text or images.
The kernel trick lets SVMs handle curved, non-linear data by mathematically "pretending" the data exists in a higher dimension where it can be separated with a straight line. Instead of actually moving the data to higher dimensions (which would be very slow), kernel trick functions do the math behind the scenes to make this work. This allows SVMs to create curved boundaries in your original data while still using their standard straight-line methods.
Support vector regression (SVR) uses the same basic SVM approaches, but instead of drawing a line to separate categories, it draws a line that best fits through data points to predict numbers. The key difference is that SVR creates a margin of acceptable error around the prediction line: As long as actual values fall within the margin of error, they're considered good predictions.
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