SVM in machine learning is an advanced model used to solve complex classification, regression or other problems. It determines boundaries between the data points based on predefined classes such as labels, classes, etc.
This mathematical equation is used to handle linear and non linear classification tasks. In this article, let us learn more about SVM in machine learning and its uses.
What is SVM in Machine Learning?
A Support Vector Machine or Svm in machine learning is a powerful model or algorithm used to analyse complex problems and data for regression analysis and classification.
Support Vector Machine is widely used in major disciplines such as Healthcare, speech recognition, image recognition, natural language processing, signal processing, and more. SVM is more often used for attending classification tasks rather than regression.
Their main objective is to find the best possible line, decision boundary or a hyperplane in a N-dimensional space which can be used to separate data points in different classes in the space. The number of features determine the value of N-dimensional space.
Also, check, Types of Regression Analysis in Machine Learning
Important Terminologies in Support Vector Machine Algorithm
Some of the most important terminologies which you must be aware of in SVM are machine learning algorithms.
- Support Vectors: The closest data point to the hyperplane is known as support vectors. They are used to determine the maximum distance between data points and the hyperplane.
- Hyperplane: The hyperplane is a boundary used to decide boundaries that separates data points into different classes. They are used to classify data in Supervised machine learning models like SVM.
- Margin: It is the distance between the support vectors and the hyperplane in a future space. Wider the margin better is the classification.
- Hard Margin: It is the maximum margin hyperplane that separates the data points of different classes in the n-dimensional space. It is calculated by finding the maximum distance between the hyperplane and data points.
- Kernel: Kernel is a mathematical function used for pattern analysis i,e. Transforming data into a processable format. Kernels find a hyperplane that can separate data points based on different classes.
- Kernel Function: Kernel function is used to evaluate the similarity score between data points based on their input distance in the future space. Higher value is assigned to points closer to each other and lower values for points that are at a distance or farther apart.
Also, check, Top Machine Learning Models used In 2024
Working of Support Vector Machine Algorithm
Support Vector Machine is used to find the maximum separation margin between the two classes. They are calculated using the maximum distance between the hyperplane and the nearest data point on each side of the plane.
For example, let us suppose there are two features x1 and x2. We can draw multiple lines which can separate data points and lines on the plane. However, we need to select the hyperplane which maximises the separation margin between the two data points.
We choose the hyperplane whose distance is maximised on each side is maximised from the nearest data points. Such a hyperplane is known as hard margin or maximum margin hyperplane.
Also, check, Data Science Vs Machine Learning 2024
Mathematical Computation for SVM in Machine Learning
Check the step by step mathematical computation for Support Vector Machine in Machine Learning Algorithm below.
1. Define the Hyperplane
For a linear SVM, we define a hyperplane in n-dimensional space as:
| w⋅x+b=0w⋅x+b=0 |
where:
- w is the weight vector,
- x is the input feature vector,
- b is the bias term.
2. Objective Function
The goal of SVM in machine learning is to maximise the margin between the classes. The margin is defined as the distance between the hyperplane and the nearest data points from each class. These nearest points are known as support vectors.
For separable data, we have the following constraint for each data point (xi,yi)
| yi(w⋅xi+b)≥1 |
where yi is the class label, which can either be +1 or −1.
3. Maximizing the Margin
The margin is 2/ ||w||, so to maximize it, we minimise ||w||2 (for mathematical convenience). Therefore, the optimization problem becomes:
Minimize ½ ||w||2
subject to:
yi(w⋅xi+b)≥1,∀i
4. Introducing the Lagrange Multipliers
To solve this constrained optimization, we use the Lagrangian with multipliers αi≥0
We maximize this Lagrangian with respect to αi, subject to αi≥0 and ∑iαiyi=0
5. Solving the Dual Problem
This formulation leads to a dual optimization problem, where:
After solving for α, we compute:
and select bb using any support vector xixi such that:
yi (w⋅xi+ b)= 1
6. Classification Decision
Given a new point x, we classify it based on the sign of:
f(x) = w⋅x+ b
If f(x)>0, then x belongs to class +1; if f(x)<0, then x belongs to class −1.
Types of SVM in Machine Learning
There are two major types of Support vector machine based on the type of arrangements of data points in the plane.
Linear SVM in Machine Learning
This machine learning creates a straight line hyperplane which separates different classes or data points in the plane. LVM is computationally efficient and has better interpretability. It is also known as linearly separable data or Linear SVM Classifier in Machine Learning.
Non-Linear SVM in Machine Learning
A non-linear Support vector machine is a type of SVM which does not create a single straight line to separate data points of different classes and hyperplanes. It uses kernel functions to transform linear non-linear spaces to linear spaces.
Implementing SVM Algorithm in Python
Check a simple implementation of svm algorithm with a sample dataset below.
| import numpy as np
from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score, classification_report import matplotlib.pyplot as plt # Load the dataset iris = datasets.load_iris() X = iris.data[:, :2] # Use only the first two features for easy visualization y = iris.target # 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) # Create and train the SVM model svm = SVC(kernel=’linear’, C=1) # Using a linear kernel for simplicity svm.fit(X_train, y_train) # Make predictions on the test set y_pred = svm.predict(X_test) # Evaluate the model print(“Accuracy:”, accuracy_score(y_test, y_pred)) print(“Classification Report:\n”, classification_report(y_test, y_pred)) # Function to plot decision boundary (Optional for visualization) def plot_decision_boundary(X, y, model): h = .02 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 = model.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.contourf(xx, yy, Z, alpha=0.3) plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors=’k’, marker=’o’) plt.xlabel(‘Feature 1’) plt.ylabel(‘Feature 2’) plt.show() # Plot the decision boundary plot_decision_boundary(X, y, svm) |
Applications of Support Vector Machines (SVM) in Machine Learning
SVM in machine learning can be used to solve many real world problems due to its high computational power and efficiency.
- SVM algorithm can be used to identify faces from images frequently used in identity authorization or verification. Nowadays it is widely used in autonomous driving or surveillance, helping in detecting objects while driving.
- SVM algorithm can also be used to detect and recognise a handwritten content by training on a labelled datasets.
- It is highly used in spam detection and sentiment analysis often applied to collect customer feedback and make the platform spam free.
- SVM in machine learning is highly integrated in finance and stock market analysis. It is used to analyse patterns based on historical data. Also, it can detect any fraud activities by mapping the patterns with an unidentified anomaly on the system.
- It can make informed investment decisions based on the stock movements (analysing the past few days data).
- SVM algorithms can be used to implement Natural Language Processing and help in classifying words in sentences, syntactic analysis, language recognition, name recognition, anomaly detection and more.
- It is also used to make weather forecasting, customer demand forecasting for businesses, and other types of predictive forecasting based on the available data.
Advantages and Disadvantages of SVM in Machine Learning
Some of the major advantages and disadvantages of svm in machine learning algorithm are mentioned below.
Pros of SVM in Machine Learning
Some of the major advantages of using svm machine learning frameworks are mentioned below.
- Svm algorithms can perform well in high dimensional spaces especially when the number of features is larger than the given number of samples.
- It uses Kernel functions to handle non linear relationships in data such as Sigmoid, Gaussian Radial Basis Function (RBF), Polynomial, etc.
- It can be used in image classification, sentiment analysis, fraud detection, and more.
- SVM algorithms are less prone to overfitting like other machine learning models.
- It can also produce accurate results when the training data set is very small.
- SVM in machine learning can be used in solving regression as well as classification problems.
Cons of SVM in Machine Learning
- The training time for svm algorithms in case of large datasets might be much higher.
- It can also be costly when dealing with large datasets as memory requirements and training time both increase.
- Implementing svm in real time might get difficult.
- Choosing the right kernel is important and it might become one of the most tedious tasks.
- SVM algorithms might struggle in generalization of overlapping classes.
- They are very less interpretable as other machine learning algorithms.
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SVM in Machine Learning FAQs
Q1. What is SVM in machine learning?
Ans: Support Vector Machine or SVM in machine learning is an advanced computing algorithm used to analyse complex problems and data for regression analysis and classification.
Q2. What are the two types of SVM in machine learning?
Ans: The two types of machine learning algorithm are Linear SVM and Non-linear SVM. Linear SVM in machine learning creates a straight line decision boundary while non-linear uses kernel functions to map the data into higher dimensional space.
Q3. What are the key advantages of using SVM in machine learning?
Ans: SVM algorithm is a powerful algorithm used for handling linear and nonlinear relationships. It can ignore outliers to make more accurate spam detection, anomaly detection. Support vectors make SVM more memory efficient as compared to other algorithms.
Q4. What type of kernel functions are used in SVM?
Ans: Linear, polynomial, radial basis function, sigmoid are some of the type of kernel functions used in Support vector machines.
