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The apply functions that you can choose depend on the models that you apply. Many of these functions have parameters described in Apply Functions Parameters.
These are the apply functions, arranged according to model:
Clustering Models
Cluster ID: returns s NUMBER of the most probable cluster ID. If the cluster ID has been renamed, then a VARCHAR2 is returned instead.
Cluster Probability: Returns a measure of the degree of confidence of membership (NUMBER) of an input row in a cluster associated with the specified model.
Cluster Set: Returns a varray of objects containing all possible clusters that a given row belongs to given the parameter specifications. Each object in the varray is a pair of scalar values containing the cluster ID and the cluster probability. The object fields are named CLUSTER_ID and PROBABILITY, and both are Oracle NUMBER. Clustering models only.
NMF Models
Feature ID: Returns an Oracle NUMBER that is the identifier of the feature with the highest value for the row.
Feature Set: Same as Cluster Set.
Feature Value: Returns the value of a given feature. If you omit the feature ID argument, then the function returns the highest feature value.
Classification and Regression Models
Prediction: Returns the best prediction for the model. The data type returned depends on the target value type used during the build of the model. For regression models, this function returns the expected value. The function returns the lowest cost prediction using the stored cost matrix if a cost matrix exists. If no stored cost matrix exists, then the function returns the highest probability prediction.
Prediction Bounds: For generalized linear models, returns an object with two NUMBER fields LOWER and UPPER. For a regression mining function, the bounds apply to value of the prediction. For a classification mining function, the bounds apply to the probability value. If the GLM was built using ridge regression, or if the covariance matrix is found to be singular during the build, then this function returns NULL for both fields.
Prediction Lower Bounds: Same as Prediction Bounds but only returns the lower bounds as a scalar column.
Prediction Upper Bounds: Same as Prediction Bounds but only returns the upper bounds as a scalar column.
Classification
Prediction Costs: Returns a measure of cost for a given prediction as a NUMBER.Classification models only.
Prediction Probability: Returns the probability associated with the best prediction. Classification models only.
Prediction Set: Returns a varray of objects containing all classes in a multiclass classification scenario. The object fields are named PREDICTION, PROBABILITY, and COST. The data type of the PREDICTION field depends on the target value type used during the build of the model. The other two fields are both Oracle NUMBER. The elements are returned in the order of best prediction to worst prediction. Classification models only.
Decision Tree
Prediction Details: Returns an XML string containing model-specific information related to the scoring of the input row. In this release, the return value is in the form <Node id = "integer"/>
. Decision Tree only.