Support Vector Machine, Support Vector Regression, Support Vector Classifier, Support Vectors, Machine Learning.
disadvantages: Advantages: SVM works well with high-dimensional data and can handle a large number of input features. SVM can handle both linearly separable and non-linearly separable data by using kernel functions. SVM has a good generalization ability, which means it can accurately classify