SVM Implementation Code with Python Example

In the following content, we provide a program code implementation for the Support Vector Machine (SVM) algorithm. SVM is a supervised learning classification algorithm primarily designed to separate samples into distinct categories. Key advantages of this algorithm include its ability to handle high-dimensional data and perform well with small datasets. Below is the code implementation demonstrating the algorithm:

# Import required libraries from sklearn import svm from sklearn.datasets import make_blobs # Create a sample dataset using make_blobs # n_samples=100 generates 100 data points, centers=2 creates two clusters # random_state=6 ensures reproducible results X, y = make_blobs(n_samples=100, centers=2, random_state=6) # Define SVM model with linear kernel and regularization parameter C=1000 # The linear kernel works well for linearly separable data # C parameter controls the trade-off between margin maximization and classification error clf = svm.SVC(kernel='linear', C=1000) # Train the model using the generated dataset # The fit method learns the optimal hyperplane that separates the two classes clf.fit(X, y) # Make predictions on new data points # The predict method classifies the input sample [10, 10] into one of the categories print(clf.predict([[10, 10]]))

Furthermore, algorithm accuracy can be improved by tuning parameters such as kernel type and regularization strength. Cross-validation techniques can be employed to evaluate algorithm performance and prevent overfitting. This code example should help you better understand the implementation process of the SVM algorithm and serve as a foundation for more complex applications.