Support Vector Machines (SVM) In Machine Learning Made Simple & How To Tutorial

by | May 6, 2024 | Uncategorized

What are Support Vector Machines?

Machine learning algorithms transform raw data into actionable insights. Among these algorithms, Support Vector Machines (SVMs) stand out as a core algorithm for supervised learning. They are versatile and effective in classification and regression tasks, crucial in various real-world scenarios such as fraud detection, image recognition, and text classification, and have made them indispensable tools for data scientists and machine learning engineers.

At its core, SVMs offer a robust framework for analysing and categorising data points into distinct classes. By finding the optimal hyperplane that best separates different classes while maximising the margin between them, SVMs handle linear and non-linearly separable datasets. This unique capability and their robust mathematical foundation have propelled SVMs to the forefront of various applications across industries, from text classification and image recognition to bioinformatics and financial forecasting. Understanding SVMs can open up possibilities in your data analysis journey.

Understanding the Support Vector Machine Algorithm

Support Vector Machines (SVMs) are a cornerstone of machine learning, offering a robust framework for classification and regression tasks. At the heart of SVMs lies a conceptually simple yet powerful idea: finding the optimal hyperplane that best separates different classes in a dataset while maximising the margin between them. This elegant simplicity is what makes SVMs so powerful and widely used in the field of machine learning.

Support vector Machines (SVM) work with decision boundaries

Intuition Behind SVMs

SVMs operate by identifying the hyperplane that best separates data points belonging to different classes in a high-dimensional space.

The key objective is to maximise the margin, the distance between the hyperplane and the nearest data points from each class, known as support vectors.

By maximising the margin, SVMs promote robust generalisation and enhance the model’s ability to classify unseen data accurately.

Components of SVMs

  • Hyperplane: The decision boundary that separates different classes in the feature space.
  • Support Vectors: Data points that lie closest to the hyperplane and influence its position.
  • Margins: The perpendicular distance between the hyperplane and the support vectors is crucial for determining the generalisation capacity of the model.

Visual Representation

In a simple linearly separable case, Support Vector Machines (SVMs) illustrate their core concept by delineating a hyperplane and identifying support vectors.

Hyperplane and Support Vectors

In this visual representation, imagine a scatter plot of data points belonging to two distinct classes. The hyperplane serves as the decision boundary, effectively segregating these classes. It is positioned in such a way that it maximizes the margin, which is the perpendicular distance between the hyperplane and the nearest data points from each class. These crucial data points, known as support vectors, are pivotal in defining the hyperplane. By leveraging these support vectors, SVMs optimize the decision boundary’s placement, ensuring robust classification performance.

Support vector machine svm decision margin

Computing the Margin: The margin, as mentioned, represents the distance between the hyperplane and the support vectors. Maximizing this margin is imperative for enhancing the SVM’s generalisation ability to unseen data. A larger margin signifies a clearer class separation, reducing the risk of misclassification and overfitting. Through meticulous optimization, SVMs strive to balance maximizing the margin and minimizing classification errors, leading to better generalization and improved performance on unseen data.

Why Maximizing the Margin Matters

Maximizing the margin is a regularization mechanism, helping SVMs resist the temptation to fit the training data too closely. By prioritizing a wide margin, SVMs prioritize simplicity and generalization, thus reducing the likelihood of memorizing noise or outliers in the training data. This emphasis on margin maximization aligns with the broader objective of machine learning—to learn patterns that generalize well to unseen data rather than merely memorizing the training set.

Linear and Non-linear Separability

Support Vector Machines (SVMs) are adept at handling datasets with varying degrees of separability. While linear SVMs excel in scenarios where classes are linearly separable, non-linear SVMs leverage kernel functions to tackle complex, non-linear decision boundaries.

Linear Separability

Linearly separable datasets are characterized by classes that can be separated by a straight line or hyperplane in the feature space. Linear SVMs are well-suited for such scenarios, as they aim to find the optimal hyperplane that maximally separates the classes while maximizing the margin between them. This linear decision boundary simplifies the classification task, making it computationally efficient and easy to interpret.

Non-linear Separability

In contrast, many real-world datasets exhibit non-linear relationships between features and classes, making them non-linearly separable. Non-linear SVMs address this challenge by employing kernel functions to implicitly map the original feature space into a higher-dimensional space, where linear separation becomes feasible. By transforming the data into a higher-dimensional space, SVMs can find hyperplanes that effectively separate classes even when they are not linearly separable in the original feature space.

What are the Different Types of SVM Kernels?

Support Vector Machines (SVMs) are renowned for their ability to handle complex datasets by employing various kernel functions. These kernels are crucial in transforming data into higher-dimensional spaces, where linear separation becomes feasible.

data transformed in a kernel with an SVM

Image Source GeeksForGeeks

Understanding the different types of SVM kernels is essential for effectively tackling diverse classification and regression tasks.

1. Linear Kernel

The linear kernel is the simplest form of kernel utilized in Support Vector Machines (SVMs). It is primarily suited for datasets exhibiting linear separability.

Ideal for Linearly Separable Datasets

Linearly separable datasets are characterized by classes that can be neatly delineated by a straight line or hyperplane. The linear kernel is ideally suited for such scenarios, as it performs straightforward linear transformations on the input features, effectively mapping them into a higher-dimensional space.

Linear Transformations

By performing linear transformations, the linear kernel ensures that the decision boundary separating different classes remains a hyperplane in the feature space. This simplicity facilitates computational efficiency and enhances interpretability, as the decision boundary’s orientation is aligned with the original feature axes.

Performance Considerations

While the linear kernel excels in scenarios where classes are linearly separable, its performance may degrade when faced with datasets exhibiting complex, non-linear relationships between features and classes. In such cases, the linear decision boundary may struggle to adequately capture the underlying patterns, leading to suboptimal classification performance.

2. Polynomial Kernel

The polynomial kernel is popular in Support Vector Machines (SVMs) for introducing non-linearity into the decision boundary. By computing the dot product of feature vectors raised to a specific power, the polynomial kernel enables SVMs to capture complex relationships in the data.

Introducing Non-linearity

In SVMs, the polynomial kernel transforms the original feature space into a higher-dimensional space, where non-linear relationships between features and classes can be effectively captured. The polynomial kernel introduces non-linearity by raising the dot product of feature vectors to a specific power, allowing SVMs to model decision boundaries with polynomial shapes.

Capturing Intricate Patterns

The polynomial kernel empowers SVMs to capture more intricate patterns in the data by creating decision boundaries with polynomial shapes. This flexibility enables SVMs to discern subtle variations and complex relationships between features, enhancing the model’s ability to classify data points from different classes accurately.

Degree Parameter

The degree parameter in the polynomial kernel controls the flexibility of the decision boundary. Higher degrees accommodate more complex relationships between features, allowing SVMs to capture finer details and nuances in the data. However, increasing the degree also increases the model’s complexity, which may lead to overfitting if not carefully tuned.

3. Radial Basis Function (RBF) Kernel

The Radial Basis Function (RBF) kernel stands out as one of the most popular and versatile choices in Support Vector Machines (SVMs), particularly renowned for its efficacy in handling non-linearly separable datasets.

Suitability for Non-linearly Separable Datasets

Non-linearly separable datasets pose a significant challenge for traditional linear classifiers. However, the RBF kernel addresses this challenge by leveraging a Gaussian radial basis function to map the original feature space into an infinite-dimensional space. In this higher-dimensional space, complex relationships between features and classes can be effectively captured, enabling SVMs to discern intricate patterns and delineate non-linear decision boundaries.

Utilization of Gaussian Radial Basis Function

The key to the RBF kernel’s effectiveness lies in utilising the Gaussian radial basis function. This function computes the similarity between data points, with closer points exhibiting higher similarity. By leveraging this notion of similarity, the RBF kernel facilitates identifying complex relationships in the data, allowing SVMs to make accurate classifications even in the presence of non-linearities.

Controlled by the Gamma Parameter

The gamma parameter in the RBF kernel plays a crucial role in controlling the influence of individual training samples on the decision boundary. Higher values of gamma result in tighter clustering of support vectors around each data point, effectively making the decision boundary more sensitive to local variations in the data. While higher gamma values can lead to improved accuracy, there’s a risk of overfitting, as the model may become overly complex and struggle to generalize to unseen data.

4. Sigmoid Kernel

The Sigmoid kernel is a distinctive variant in Support Vector Machines (SVMs). It employs a sigmoid function to transform data and offers an alternative approach to capturing non-linear relationships.

sigmoid function

The sigmoid function

Transforming Data with Sigmoid Function

Unlike traditional kernel functions, the Sigmoid kernel applies a sigmoidal transformation to the input data. This transformation allows SVMs to capture non-linear relationships in the data by mapping it into a higher-dimensional space. The sigmoid function introduces flexibility in modelling decision boundaries, enabling SVMs to delineate complex patterns with S-shaped curves.

Binary Classification Tasks

Initially developed for binary classification tasks, the Sigmoid kernel is adept at modelling decision boundaries with S-shaped curves. These curves allow SVMs to effectively separate two classes by capturing non-linear relationships between features and class labels. While originally designed for binary classification, the Sigmoid kernel can also be adapted for multi-class classification tasks through techniques like one-vs-all or one-vs-one.

Effectiveness in Specific Scenarios

While the Sigmoid kernel may be less commonly used than other kernels like linear, polynomial, or radial basis function (RBF), it can be highly effective in specific scenarios. The Sigmoid kernel is particularly well-suited for datasets where the underlying relationships exhibit sigmoidal behaviour, such as certain biological or economic phenomena. In such cases, the Sigmoid kernel can capture the inherent non-linearities in the data and yield accurate classification results.

5. Custom Kernels

Support Vector Machines (SVMs) offer the flexibility to utilize custom kernels tailored to the data’s unique characteristics in addition to predefined kernels.

Flexibility in Modeling Complex Relationships

Custom kernels empower practitioners to capture complex relationships in the data that may not be adequately represented by predefined kernels like linear, polynomial, or radial basis functions (RBF). By designing custom kernels, SVMs can adapt to the intricacies of different datasets and extract meaningful patterns, thereby enhancing classification performance and model interpretability.

Designed Based on Domain Knowledge or Empirical Insights

Custom kernels can be crafted based on domain knowledge, empirical insights, or specific requirements of the task. Practitioners can leverage their expertise to design kernels that encapsulate relevant information about the data’s structure, relationships, or underlying phenomena. This tailored approach ensures that the SVM model is optimized for the specific nuances of the problem domain, leading to improved performance and insights.

Enhanced Flexibility and Expressiveness

Custom kernels offer enhanced flexibility and expressiveness compared to predefined kernels, allowing practitioners to experiment with various kernel functions and parameters. This flexibility enables the exploration of novel data representation and modelling approaches, unlocking new possibilities for solving complex machine-learning tasks.

Adaptation to Diverse Data Characteristics

SVMs with custom kernels can adapt to diverse data characteristics, including non-linearities, asymmetries, and heterogeneities. By incorporating domain-specific knowledge into the kernel design, practitioners can effectively address challenges unique to their datasets and extract meaningful insights that may not be attainable with standard kernel functions.

By leveraging a diverse range of SVM kernels, we can adapt to the intricacies of different datasets and extract meaningful patterns even from highly complex and non-linear data distributions. In the next section, we delve into the mathematical formulations underlying SVM kernels and explore practical considerations for selecting the most suitable kernel for a given task.

Mathematics Behind The Support Vector Machines Algorithm

Support Vector Machines (SVMs) are grounded in robust mathematical principles, making them powerful and reliable tools for classification and regression (SVR) tasks. Understanding the mathematical formulations that underpin SVMs is essential for gaining insights into their inner workings and effectively leveraging their capabilities in practice.

Optimisation Problem

SVMs aim to find the optimal hyperplane that maximally separates different classes while maximising the margin.

This is achieved by solving an optimisation problem that minimises the classification error while maximising the margin.

The optimisation problem can be expressed as a constrained optimisation using Lagrange multipliers.

Lagrange Multipliers and the Dual Problem

In SVMs, the goal is to find the optimal hyperplane that maximally separates different classes while maximizing the margin. This task can be formulated as a constrained optimization problem, where the margin is maximized subject to certain constraints defined by the training data.

Role of Lagrange Multipliers

Lagrange multipliers enable the conversion of this constrained optimization problem into an unconstrained one by incorporating the constraints directly into the objective function. By introducing Lagrange multipliers for each constraint, the original objective function can be augmented to include penalty terms that penalize violations of the constraints.

Derivation of the Dual Problem

The augmented objective function, incorporating Lagrange multipliers, leads to the formulation of the Lagrangian, which involves maximizing a function with respect to both the primal variables (hyperplane parameters) and the Lagrange multipliers. This process results in the derivation of the dual problem, where the goal is to maximize the Lagrangian with respect to the Lagrange multipliers subject to certain constraints.

Solving the Dual Problem

Solving the dual problem yields the optimal solution for the SVM, including the support vectors and the corresponding coefficients. The support vectors are the data points closest to the decision boundary and are crucial in defining the hyperplane. By optimizing the dual problem, SVMs effectively identify the support vectors and determine the optimal hyperplane parameters that maximize the margin between different classes.

Kernel Trick

The kernel trick allows SVMs to operate in higher-dimensional feature spaces without explicitly computing the transformation.

Instead of computing the dot product in the transformed space, kernels directly compute the similarity between data points in the original feature space.

Common kernel functions such as the polynomial, radial basis function (RBF), and sigmoid kernels enable SVMs to handle non-linear relationships effectively.

Mathematical Formulation

The mathematical formulation of SVMs involves defining the decision function that separates different classes based on the input features.

For linear SVMs, the decision function is a linear combination of the input features weighted by the coefficients obtained during training.

For non-linear SVMs, the decision function incorporates the kernel function to map data into a higher-dimensional space where linear separation is feasible.

Margin Maximization

Maximising the margin between different classes is crucial for enhancing the generalisation performance of SVMs.

The margin represents the perpendicular distance between the hyperplane and the support vectors; the data points closest to the decision boundary.

By maximising the margin, SVMs promote robust classification performance and minimise the risk of overfitting the training data.

Understanding the mathematical foundations of SVMs provides a solid basis for effectively applying them to various machine-learning tasks.

In the next section, we delve into the practical aspects of training SVM models and fine-tuning hyperparameters to achieve optimal performance.

Training Support Vector Machines

Training a Support Vector Machine (SVM) model involves several steps to find the optimal hyperplane that best separates different classes in the dataset while maximising the margin between them. In this section, we explore the process of training an SVM model, including data preprocessing, model training, and hyperparameter tuning.

1. Data Preprocessing:

Feature Scaling: SVMs are sensitive to the scale of input features, so it’s crucial to scale or normalise them to ensure a level playing field.

Feature Selection: Identifying and selecting relevant features can improve the efficiency and performance of the SVM model, especially in high-dimensional datasets.

Handling Imbalanced Data: Addressing class imbalance through techniques such as oversampling, undersampling, or using class weights can enhance the model’s generalisation ability.

2. Model Training:

Choosing the Kernel: Selecting the appropriate kernel function based on the data’s characteristics is critical. This decision depends on whether the data is linearly separable or requires non-linear transformations.

Training the Model: Using training data, the SVM algorithm optimises the hyperplane parameters and identifies the support vectors that define the decision boundary.

Regularisation: Regularisation parameters, such as C for soft margin SVMs, control the trade-off between maximising the margin and minimising classification errors. Tuning these parameters is essential for achieving the right balance and avoiding overfitting.

3. Hyperparameter Tuning:

Cross-Validation: Employing k-fold cross-validation helps assess the SVM model’s generalisation performance across different parameter configurations.

Grid Search: An exhaustive grid search over a predefined set of hyperparameters allows for identifying the combination that yields the best performance based on a chosen evaluation metric.

Model Evaluation: Evaluating the trained SVM model using appropriate performance metrics such as accuracy, precision, recall, F1-score, or area under the ROC curve (AUC) provides insights into its effectiveness and generalisation ability.

4. Model Interpretation:

Visualising Decision Boundaries: Visualising the decision boundaries and support vectors can provide intuitive insights into how the SVM model separates different classes in the feature space.

Feature Importance: Analysing the coefficients or weights assigned to different features can help understand which features contribute most to the classification decision.

5. Optimisation Techniques:

Stochastic Gradient Descent (SGD): Utilising SGD-based optimisation techniques can accelerate the training process, especially for large-scale datasets.

Parallelisation: Leveraging parallel computing frameworks can distribute the computational workload and expedite model training, enabling faster iterations and experimentation.

By following these steps and leveraging optimisation techniques, we can effectively train SVM models that generalise well to unseen data and deliver reliable performance across various machine learning tasks. In the next section, we explore real-world applications of SVMs and showcase their versatility in solving complex problems across different domains.

generalise well to unseen data and deliver reliable performance across various machine learning tasks.

How To Implement a Support Vector Machines In Python

Let us look at a basic example of how to implement a Support Vector Machine (SVM) classifier in Python using the popular machine learning library scikit-learn:

# Importing necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = datasets.load_iris()
X =[:, :2]  # We'll only use the first two features for visualization purposes
y =

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Initialize SVM classifier
svm_classifier = SVC(kernel='linear', C=1, random_state=42)

# Train the classifier, y_train)

# Make predictions on the test data
y_pred = svm_classifier.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# Visualize decision boundaries
# Function to plot decision boundaries
def plot_decision_boundary(X, y, classifier):
    h = .02  # step size in the mesh
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    Z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.contourf(xx, yy, Z,, alpha=0.8)

    # Plot the training points
    plt.scatter(X[:, 0], X[:, 1], c=y,, edgecolors='k')
    plt.xlabel('Sepal length')
    plt.ylabel('Sepal width')
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title('SVM Decision Boundary')

# Plot decision boundary
plot_decision_boundary(X_train, y_train, svm_classifier)
Support vector machine SVM plot

We start by loading the Iris dataset, splitting it into training and testing sets, initialising an SVM classifier with a linear kernel, training the classifier on the training data, making predictions on the test data, calculating accuracy, and visualising the decision boundary. You can experiment with kernels (e.g., ‘linear’, ‘rbf’, ‘poly’) and hyperparameters to see how they affect the model’s performance and decision boundary.

Practical Applications of SVMs

Support Vector Machines (SVMs) have found widespread applications across various domains due to their ability to effectively handle linear and non-linearly separable datasets. This section explores some practical applications where SVMs have demonstrated remarkable performance and versatility.

1. Text Classification:

SVMs are extensively used for text classification tasks such as sentiment analysis, spam detection, and topic categorisation.

By representing text data as high-dimensional feature vectors using techniques like TF-IDF (Term Frequency-Inverse Document Frequency), SVMs can effectively learn to classify documents into different categories.

2. Image Recognition:

SVMs are employed in image recognition tasks, including object detection, face recognition, and handwritten digit recognition.

By extracting features from images and representing them as input vectors, SVMs can learn to accurately differentiate between objects or classes.

3. Bioinformatics:

SVMs are widely used in bioinformatics for protein classification, gene expression analysis, and disease diagnosis tasks.

SVMs can effectively handle high-dimensional biological data and learn complex patterns, which can help understand biological processes and identify disease-associated biomarkers.

4. Financial Forecasting:

SVMs are applied in financial forecasting tasks such as stock price prediction, credit scoring, and fraud detection.

SVMs can help predict future market trends or detect fraudulent transactions by analysing historical financial data and identifying patterns.

5. Medical Diagnosis:

SVMs are crucial in medical diagnosis tasks, including disease classification, patient outcome prediction, and medical image analysis.

By analysing patient data such as clinical features, genetic markers, or medical images, SVMs can assist healthcare professionals in making accurate diagnoses and treatment decisions.

6. Natural Language Processing (NLP):

SVMs are employed in various NLP tasks, including named entity recognition, part-of-speech tagging, and text summarisation.

By learning from labelled text data, SVMs can effectively classify and extract relevant information from unstructured text, facilitating language understanding and information retrieval.

7. Computer Vision:

SVMs are used in computer vision applications such as object detection, image segmentation, and image classification.

By learning to differentiate between different visual patterns or objects, SVMs can enable machines to interpret and understand visual information in real-world environments.

8. Remote Sensing:

SVMs find applications in remote sensing tasks such as land cover classification, crop yield prediction, and environmental monitoring.

By analysing satellite imagery and extracting meaningful features, SVMs can assist in mapping and monitoring changes in the Earth’s surface over time.

Support Vector Machines (SVMs) have emerged as versatile tools with various practical applications across industries. Their ability to handle complex datasets and learn intricate patterns makes them invaluable assets in machine learning and data-driven decision-making. As technology advances, SVMs are expected to be increasingly important in addressing diverse challenges and driving innovation across various domains.


Support Vector Machines (SVMs) have emerged as a cornerstone in machine learning, offering a robust framework for classification and regression tasks. Because they can handle linear and non-linearly separable datasets, SVMs have widespread applications across diverse domains, ranging from text classification and image recognition to bioinformatics and financial forecasting.

In this comprehensive guide, we’ve delved into the inner workings of SVMs, exploring their underlying principles, mathematical formulations, and practical implementations. We’ve discussed the importance of understanding SVM kernels, hyperparameters, and optimisation techniques for achieving optimal performance. From data preprocessing and model training to hyperparameter tuning and model interpretation, we’ve provided valuable insights and strategies for effectively working with SVMs.

As technology advances and datasets grow in complexity, SVMs remain at the forefront of machine learning, driving innovation and enabling solutions to real-world challenges. By embracing the tips and tricks outlined in this guide and continuously exploring new methodologies and techniques, we can leverage SVMs to unlock new possibilities and push the boundaries of what’s achievable in artificial intelligence.

In the journey towards harnessing the full potential of SVMs, experimentation, collaboration, and a commitment to lifelong learning will be essential. As we navigate the ever-evolving landscape of machine learning, SVMs stand as steadfast allies, empowering us to transform data into knowledge, insight, and impact.

The future of SVMs is bright, and with each breakthrough and discovery, we move one step closer to realising this remarkable machine learning paradigm’s transformative potential.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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