Support Vector Machine, Support Vector Regression, Support Vector Classifier, Support Vectors, Machine Learning.
imbalance of the number of samples in each class. In such cases, we need to use techniques such as oversampling or undersampling to balance the classes. SVM can be difficult to interpret, especially when using non-linear kernels. The decision function is a complex combination of the input features,