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The apply functions that you can choose depend on the models that you apply. Function parameters are described in Apply Functions Parameters.
Note: Certain Apply functions are available only if you are connected to Oracle Database 12c . |
These are the apply functions, arranged according to model node:
Anomaly Detection Models
Prediction: An automatic setting that 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 Details: Requires connection to Oracle Database 12c
. Returns prediction details. The return value describes the attributes of the prediction. For anomaly detection, the returned details refer to the highest probability class or the specified class value.
The defaults for Predictions Details are as follows:
Target Value: Most Likely
Sort by Weights: Absolute value
Maximum Length of Ranked Attribute List: 5
Prediction Details output is in XML format (XMLType data type). You must parse the output to find the data that you need.
Prediction Probability: An automatic setting that returns the probability associated with the best prediction.
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.
Clustering Models
Cluster Details: Requires connection to Oracle Database 12c
. The return value describes the attributes of the highest probability cluster or the specified cluster ID. If you specify a value for topN, the function returns the N attributes that most influence the cluster assignment (the score). If you do not specify topN, the function returns the 5 most influential attributes.
The defaults for Predictions Details are as follows:
Cluster Id: Most Likely
Sort by Weight: Absolute value
Maximum Length of Ranked Attribute List: 5
The returned attributes are ordered by weight. The weight of an attribute expresses its positive or negative impact on cluster assignment. A positive weight indicates an increased likelihood of assignment. A negative weight indicates a decreased likelihood of assignment.
Cluster Details output is in XML format (XMLType data type). You must parse the output to find the data that you need.
Cluster Distance: Requires connection to Oracle Database 12c
. Returns a cluster distance for each row in the selection. The cluster distance is the distance between the row and the centroid of the highest probability cluster or the specified cluster ID.
The defaults for Predictions Details are as follows:
Cluster Id: Most Likely
Cluster ID: An automatic setting that returns the NUMBER of the most probable cluster ID. If the cluster ID has been renamed, then a VARCHAR2 is returned instead.
Cluster Probability: An automatic setting that 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
Feature Extraction Models
Feature ID: Returns an Oracle NUMBER that is the identifier of the feature with the highest value for the row.
Feature Set: An automatic setting that is similar to 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.
Feature Details: Requires connection to Oracle Database 12c
. The return value describes the attributes of the highest value feature or the specified feature ID. If you specify a value for topN, the function returns the N attributes that most influence the feature value. If you do not specify topN, the function returns the 5 most influential attributes.
The returned attributes are ordered by weight. The weight of an attribute expresses its positive or negative impact on the value of the feature. A positive weight indicates a higher feature value. A negative weight indicates a lower feature value.
The defaults for Predictions Details are as follows:
Feature Id: Most Likely
Sort by Weight: Absolute value
Maximum Length of Ranked Attribute List: 5
Feature Details output is in XML format (XMLType data type). You must parse the output to find the data that you need.
Classification and Regression Models
Prediction: An automatic setting that 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. For classification models, the returned details refer to the highest probability class or the specified class 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 Bounds Lower: Same as Prediction Bounds but only returns the lower bounds as a scalar column. Automatic Setting for GLM models.
Prediction Bounds Upper: Same as Prediction Bounds but only returns the upper bounds as a scalar column. Automatic Setting for GLM models.
Prediction Details: Requires connection to Oracle Database 12c
except for Decision Tree.
The defaults for Predictions Details for Classification are as follows:
Target Value: Most Likely
Sort by Weights: Absolute value
Maximum Length of Ranked Attribute List: 5
The defaults for Predictions Details for Regression are as follows:
Sort by Weights: Absolute value
Maximum Length of Ranked Attribute List: 5
DT Prediction Details: Requires connection to Oracle Database 11g Release 2 (11.2). Returns a string containing model-specific information related to the scoring of the input row. In Data Miner releases before 4.0, the return value is in the form <Node id = "integer"/>
.
Classification
Prediction Costs: Returns a measure of cost for a given prediction as a NUMBER.Classification models only. Automatic Setting for DT models.
Prediction Probability: Returns the probability associated with the best prediction. Automatic Setting for most likely.
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.