What is Support Vector Machine (SVM)?

What is Support Vector Machine (SVM)?

Support Vector Machine is a supervised learning algorithm primarily used for classification tasks. SVM aims to find the hyperplane that best separates the classes in the feature space. It operates by mapping the input data onto a high-dimensional feature space and then determining the optimal hyperplane that maximizes the margin in svm between classes. SVM can handle both linear and non-linear classification through the use of different kernel functions such as linear, polynomial, or radial basis function (RBF). SVM is known for its effectiveness in high-dimensional spaces and its ability to handle complex decision boundaries....

What is Extreme Gradient Boosting (XGBoost)?

Extreme Gradient Boosting, often abbreviated as XGBoost, is a popular ensemble learning algorithm known for its efficiency and effectiveness in classification and regression tasks. XGBoost belongs to the family of gradient boosting algorithms, which works by sequentially combining weak learners (typically decision trees) to create a strong learner. It minimizes a loss function by adding new models that predict the residuals or errors made by the existing models. It provides better performance compared to traditional boosting algorithms by incorporating regularization techniques and parallel processing....

SVM vs XGBoost

Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) are both machine learning algorithms, but they belong to different categories and have distinct characteristics....

Which model to use: SVM or XGBoost?

Deciding between SVM and XGBoost relies on various factors such as the dataset’s properties, the problem’s nature, and your preferences regarding model performance and interpretability....

Conclusion

SVM and XGBoost are different types of algorithms with distinct strengths and weaknesses. SVM is powerful for finding optimal decision boundaries, especially in high-dimensional spaces, while XGBoost excels at capturing complex patterns in the data through the combination of weak learners....