Home > Data Mining Algorithms > Anomaly Detection > Anomaly Detection Model Vie... > Association > AR Troubleshooting, Model V... > Decision Tree > Generalized Linear Models > GLM Model Viewers and Algor... > k-Means > Naive Bayes > Non-Negative Matrix Factori... > Orthogonal Partitioning Clu... > Support Vector Machine > Building and Testing SVM Mo... > SVM Classification Models
SVM Classification (SVMC) is based on the concept of decision planes that define decision boundaries. A decision plane is one that separates between a set of objects having different class memberships. SVM finds the vectors (support vectors) that define the separators giving the widest separation of classes.
SVMC supports both binary and multiclass targets.
To build and test an SVMC model use a Classification Node. By default, the Classification Node tests the models that it builds; test data is created by splitting the input data into build and test subsets. You can also test a model using a Test Node.
SVMC uses SVM Weights to specify the relative importance of target values.