Support Vector Machine (SVM) is a powerful, state-of-the-art algorithm with strong theoretical foundations based on the Vapnik-Chervonenkis theory. SVM has strong regularization properties. Regularization refers to the generalization of the model to new data.

Learn about configuring settings for Support Vector Machines (SVM).

SVM have built-in mechanisms that automatically choose appropriate settings based on the data. You may need to override the system-determined settings for some domains.

Settings pertain to regression, classification, and anomaly detection unless otherwise specified.

The SVM algorithm operates natively on numeric attributes. SVM uses z-score normalization on numeric attributes. The normalization occurs only for two-dimensional numeric columns (not nested). The algorithm automatically "explodes" categorical data into a set of binary attributes, typically one per category value. For example, a character column for marital status with values married or single is transformed to two numeric attributes: married and single. The new attributes can have the value 1 (true) or 0 (false).

When there are missing values in columns with simple data types (not nested), SVM interprets them as missing at random. The algorithm automatically replaces missing categorical values with the mode and missing numerical values with the mean.

When there are missing values in the nested columns, SVM interprets them as sparse. The algorithm automatically replaces sparse numerical data with zeros and sparse categorical data with zero vectors.

Support Vector Machines (SVM) Classification 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.

SVM classification supports both binary, multiclass, and multitarget Classification. Multitarget alllows multiple class labels to be associated with a single row. The target type is a collection of type ORA_MINING_VARCHAR2_NT.

Oracle Data Mining uses Support Vector Machines (SVM) as the one-class classifier for anomaly detection. When SVM is used for anomaly detection, it has the classification mining function but no target.

One-class SVM models, when applied, produce a prediction and a probability for each case in the scoring data. If the prediction is 1, the case is considered typical. If the prediction is 0, the case is considered anomalous. This behavior reflects the fact that the model is trained with normal data.

You can specify the percentage of the data that you expect to be anomalous with the SVMS_OUTLIER_RATE build setting. If you have some knowledge that the number of "suspicious" cases is a certain percentage of your population, then you can set the outlier rate to that percentage. The model approximately identifies that many "rare" cases when applied to the general population.

Learn how to use epsilon-insensitivity loss function to solve regression problems in Support Vector Machines (SVM).

SVM uses an epsilon-insensitive loss function to solve regression problems.

SVM regression tries to find a continuous function such that the maximum number of data points lie within the epsilon-wide insensitivity tube. Predictions falling within epsilon distance of the true target value are not interpreted as errors.

The epsilon factor is a regularization setting for SVM regression. It balances the margin of error with model robustness to achieve the best generalization to new data.