The Support Vector Machine algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. The SVM algorithm finds a hyperplane decision boundary that best splits the examples into two classes. The split is made soft through the use of a margin that allows some points to be misclassified. By default, […]
The post Cost-Sensitive SVM for Imbalanced Classification appeared first on Machine Learning Mastery.
Comments
Post a Comment