Support Vector Machine, Svm, Svm Algorithm, Svm Classifier, Machine Learning.
data points. It’s important to note that in some cases, when the data is not linearly separable, SVM uses techniques like kernel functions to map the data into a higher-dimensional feature space, where a linear decision boundary (hyperplane) can be found. This allows SVM to handle non-linearly