Support Vector Machine (SVM)(Supervised Machine Learning)
Support Vector Machines (SVM) are powerful and versatile supervised learning algorithms used for both classification and regression tasks in machine learning. The primary goal of SVM is to find the optimal hyperplane that best separates data points of different classes in a high-dimensional feature space.
In a binary classification setting, SVM aims to find the hyperplane that maximizes the margin, the distance between the hyperplane and the closest data points of each class (known as support vectors). The margin represents the level of confidence in the classification, and SVM seeks to maximize it to improve generalization to unseen data.
SVM can handle both linearly separable and non-linearly separable data by applying the “kernel trick.” The kernel function transforms the input data into a higher-dimensional space, where it becomes linearly separable, enabling SVM to find a separating hyperplane.
The decision boundary in SVM is determined by support vectors, which are the data points closest to the hyperplane and critical for defining it. SVM uses a subset of the training data (the support vectors) to construct the decision boundary, making it memory-efficient and well-suited for large datasets.
SVM’s robustness against overfitting can be controlled by tuning the regularization parameter ©. A smaller C value results in a larger margin but allows some misclassifications, while a larger C value minimizes misclassifications but may lead to a smaller margin and potential overfitting.
Despite its effectiveness, SVM can be computationally intensive for large datasets and requires careful selection of the kernel function and tuning of hyperparameters. Additionally, SVM might not perform well when dealing with noisy data or imbalanced class distributions.
In summary, SVM is a versatile and effective algorithm for both linear and non-linear classification tasks. It is widely used in various domains such as image recognition, text classification, and bioinformatics, where it often outperforms other classifiers when properly tuned and applied to appropriate problem domains.