One-Class SVM Kernel Trick
One-Class SVM supports various kernel options like SVM for optimized performance which are discussed below:
- Linear Kernel: The linear kernel is the simplest form of a kernel and is equivalent to performing a linear transformation. It is suitable when the relationship between the features is approximately linear. The decision boundary in the higher-dimensional space is a hyperplane.
- Polynomial Kernel: The polynomial kernel introduces non-linearity by considering not just the dot product but also higher-order interactions between features. It is characterized by a user-defined degree parameter (
degree). A higher degree allows the model to capture more complex relationships but in the same time it may increase the risk of overfitting. - Sigmoid Kernel: The sigmoid kernel is particularly suitable for scenarios where the data distribution is not well defined or exhibits sigmoidal patterns. It is often used in neural network-inspired SVMs. The
gammaandcoef0parameters govern the shape and position of the decision boundary. - Radial Basis Function (RBF) or Gaussian Kernel: The RBF kernel is versatile for handling complex, non-linear relationships. It transforms data into a space where intricate decision boundaries can be drawn. Well-suited when the exact form of relationships is unknown or intricate.
- Precomputed Kernel: This kernel allows users to provide a precomputed kernel matrix instead of the actual data. Useful when the kernel matrix is computed using a custom kernel function or when using pairwise similarities between instances.
From these above kernels the RBF kernel is the default kernel for One-Class SVM. In our implementation we will use default kernel for credit card anomaly detection.
Understanding One-Class Support Vector Machines
Support Vector Machine is a popular supervised machine learning algorithm. it is used for both classifications and regression. In this article, we will discuss One-Class Support Vector Machines model.