Support Vector Machines (SVM)
Description
Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks. They work by finding the optimal hyperplane that best separates the data into different classes. SVMs aim to maximize the margin between the classes and the hyperplane, which leads to better generalization on unseen data.
How SVM Works
SVM constructs a decision boundary (hyperplane) that separates different classes. Support vectors are the data points closest to this hyperplane, and they are critical in defining the boundary.
Key characteristics:
- Effective in high-dimensional spaces
- Works well for linear and non-linear classification using kernel trick
- Robust against overfitting, especially in high-dimensional space
Examples
Python Code for SVM Classification
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report
# Load dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Train SVM model
model = SVC(kernel='linear')
model.fit(X_train, y_train)
# Predict and evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
Real-World Applications
SVM Applications
- Text Classification: Spam detection, document categorization
- Image Recognition: Handwritten digit classification (e.g., MNIST)
- Bioinformatics: Protein classification, gene expression classification
- Finance: Credit risk modeling, fraud detection
Resources
The following resources will be manually added later:
Video Tutorials
PDF/DOC Materials
Interview Questions
1. What is a Support Vector Machine?
SVM is a supervised learning algorithm used for classification and regression tasks. It finds the optimal hyperplane that separates data points of different classes with the maximum margin.
2. What is the kernel trick in SVM?
The kernel trick allows SVM to perform non-linear classification by transforming the input data into a higher-dimensional space where a linear separator can be used.
3. What are support vectors?
Support vectors are the data points that lie closest to the decision boundary. They directly influence the position and orientation of the hyperplane.
4. What is the role of the regularization parameter C in SVM?
C is a regularization parameter that controls the trade-off between maximizing the margin and minimizing the classification error. A small C creates a wider margin but allows more misclassifications, while a large C focuses on classifying all training examples correctly.
5. What are some commonly used SVM kernels?
- Linear Kernel
- Polynomial Kernel
- Radial Basis Function (RBF) Kernel
- Sigmoid Kernel