5.4 Model Detail Views
The GET_*
interfaces are replaced by model views, and Oracle recommends that users leverage the views instead.
The following are the new model views:
Association:
Classification, Regression, and Anomaly Detection:
Clustering:
Feature Extraction:
Feature Selection:
Data Preparation and Other:
Time Series:
5.4.1 Model Detail Views for Association Rules
Model detail views for Association Rules describe the rule view for Association Rules. Oracle recommends that users leverage the model details views instead of the GET_ASSOCIATION_RULES
function.
DM$VR
model_name describes the generated rules for Association Rules. Depending on the settings of the model, the rule view has different set of columns. Settings ODMS_ITEM_ID_COLUMN_NAME
and ODMS_ITEM_VALUE_COLUMN_NAME
determine how each item is defined. If ODMS_ITEM_ID_COLUMN_NAME
is set, the input format is called transactional input, otherwise, the input format is called 2Dimensional input. With transactional input, if setting ODMS_ITEM_VALUE_COLUMN_NAME
is not set, each item is defined by ITEM_NAME
, otherwise, each item is defined by ITEM_NAME
and ITEM_VALUE
. With 2Dimensional input, each item is defined by ITEM_NAME
, ITEM_SUBNAME
and ITEM_VALUE
. Setting ASSO_AGGREGATES
specifies the columns to aggregate, which is displayed in the view.
Note:
SettingASSO_AGGREGATES
is not allowed for 2dimensional input.
Transactional Input Without ASSO_AGGREGATES Setting
ITEM_NAME
(ODMS_ITEM_ID_COLUMN_NAME
) is set and ITEM_VALUE
(ODMS_ITEM_VALUE_COLUMN_NAME
) is not set, the following is the view. Here the consequent item is defined with only name field. If ITEM_VALUE
setting is also set, the view will have one extra column CONSEQUENT_VALUE
to specify the value field.Name Type
 
PARTITION_NAME VARCHAR2(128)
RULE_ID NUMBER
RULE_SUPPORT NUMBER
RULE_CONFIDENCE NUMBER
RULE_LIFT NUMBER
RULE_REVCONFIDENCE NUMBER
ANTECEDENT_SUPPORT NUMBER
NUMBER_OF_ITEMS NUMBER
CONSEQUENT_SUPPORT NUMBER
CONSEQUENT_NAME VARCHAR2(4000)
ANTECEDENT SYS.XMLTYPE
Table 511 Rule View Columns for Transactional Inputs
Column Name  Description 


A partition in a partitioned model to retrieve details 

Identifier of the rule 

The number of transactions that satisfy the rule. 

The likelihood of a transaction satisfying the rule. 

The degree of improvement in the prediction over random chance when the rule is satisfied. 

The number of transactions in which the rule occurs divided by the number of transactions in which the consequent occurs. 

The ratio of the number of transactions that satisfy the antecedent to the total number of transactions. 

The total number of attributes referenced in the antecedent and consequent of the rule. 

The ratio of the number of transactions that satisfy the consequent to the total number of transactions. 

Name of the consequent 

Value of the consequent when setting When setting 

The antecedent is described as an itemset. At the itemset level, it specifies the number of aggregates, and if not zero, the names of the columns to be aggregated (as well as the mapping to

Transactional Input With ASSO_AGGREGATES Setting

Rule view when
ODMS_ITEM_ID_COLUMN_NAME
is set andItem_value
(ODMS_ITEM_VALUE_COLUMN_NAME
) is not set. 
Rule view when
ODMS_ITEM_ID_COLUMN_NAME
is set andItem_value
(ODMS_ITEM_VALUE_COLUMN_NAME
) is set withTYPE
as numerical, the view has aCONSEQUENT_VALUE
column. 
Rule view when
ODMS_ITEM_ID_COLUMN_NAME
is set andItem_value
(ODMS_ITEM_VALUE_COLUMN_NAME
) is set withTYPE
as categorical, the view has aCONSEQUENT_VALUE
column.
For example, refer “Example: Calculating Aggregates” in Oracle Data Mining Concepts.
The view reports two sets of aggregates results:

ANT_RULE_PROFIT
refers to the total profit for the antecedent itemset with respect to the rule, the profit for each individual item of the antecedent itemset is shown in theANTECEDENT(XMLtype)
column,CON_RULE_PROFIT
refers to the total profit for the consequent item with respect to the rule.In the example, for rule (A, B) => C, the rule itemset (A, B, C) occurs in the transactions of customer 1 and customer 3. The
ANT_RULE_PROFIT
is $21.20, TheANTECEDENT
is shown as follow, which tells that item A has profit 5.00 + 3.00 = $8.00 and item B has profit 3.20 + 10.00 = $13.20, which sum up toANT_RULE_PROFIT
.<itemset NUMAGGR="1" ASSO_AGG0="profit"><item><item_name>A</item_name><ASSO_AGG0>8.0E+000</ASSO_AGG0></item><item><item_name>B</item_name><ASSO_AGG0>1.32E+001</ASSO_AGG0></item></itemset> The CON_RULE_PROFIT is 12.00 + 14.00 = $26.00

ANT_PROFIT
refers to the total profit for the antecedent itemset, whileCON_PROFIT
refers to the total profit for the consequent item. The difference betweenCON_PROFIT
andCON_RULE_PROFIT
(the same applies toANT_PROFIT
andANT_RULE_PROFIT
) is thatCON_PROFIT
counts all profit for the consequent item across all transactions where the consequent occurs, whileCON_RULE_PROFIT
only counts across transactions where the rule itemset occurs.For example, item C occurs in transactions for customer 1, 2 and 3,
CON_PROFIT
is 12.00 + 4.20 + 14.00 = $30.20, whileCON_RULE_PROFIT
only counts transactions for customer 1 and 3 where the rule itemset (A, B, C) occurs.Similarly,
ANT_PROFIT
counts all transactions where itemset (A, B) occurs, whileANT_RULE_PROFIT
counts only transactions where the rule itemset (A, B, C) occurs. In this example, by coincidence, both count transactions for customer 1 and 3, and have the same value.
Example 513 Examples
The following example shows the view when setting ASSO_AGGREGATES
specifies column profit and column sales to be aggregated. In this example, ITEM_VALUE
column is not specified.
Name Type
 
PARTITION_NAME VARCHAR2(128)
RULE_ID NUMBER
RULE_SUPPORT NUMBER
RULE_CONFIDENCE NUMBER
RULE_LIFT NUMBER
RULE_REVCONFIDENCE NUMBER
ANTECEDENT_SUPPORT NUMBER
NUMBER_OF_ITEMS NUMBER
CONSEQUENT_SUPPORT NUMBER
CONSEQUENT_NAME VARCHAR2(4000)
ANTECEDENT SYS.XMLTYPE
ANT_RULE_PROFIT BINARY_DOUBLE
CON_RULE_PROFIT BINARY_DOUBLE
ANT_PROFIT BINARY_DOUBLE
CON_PROFIT BINARY_DOUBLE
ANT_RULE_SALES BINARY_DOUBLE
CON_RULE_SALES BINARY_DOUBLE
ANT_SALES BINARY_DOUBLE
CON_SALES BINARY_DOUBLE
Rule view when ODMS_ITEM_ID_COLUMN_NAME
is set and Item_value
(ODMS_ITEM_VALUE_COLUMN_NAME
) is set with TYPE
as numerical, the view has a CONSEQUENT_VALUE
column.
Rule view when ODMS_ITEM_ID_COLUMN_NAME
is set and Item_value
(ODMS_ITEM_VALUE_COLUMN_NAME
) is set with TYPE
as categorical, the view has a CONSEQUENT_VALUE
column.
2Dimensional Inputs
In Oracle Data Mining, association models can be built using either transactional or twodimensional data formats. For twodimensional input, each item is defined by three fields: NAME
, VALUE
and SUBNAME
. The NAME
field is the name of the column. The VALUE
field is the content of the column. The SUBNAME
field is used when input data table contains nested table. In such case, the SUBNAME
is the name of the nested table's column. See, Example: Creating a Nested Column for Market Basket Analysis. In this example, there is a nested column. The CONSEQUENT_SUBNAME
is the ATTRIBUTE_NAME
part of the nested column. That is, 'O/S Documentation Set  English'
and CONSEQUENT_VALUE
is the value part of the nested column, which is, 1.
The view uses three columns for consequent. The rule view has the following columns:
Name Type
 
PARTITION_NAME VARCHAR2(128)
RULE_ID NUMBER
RULE_SUPPORT NUMBER
RULE_CONFIDENCE NUMBER
RULE_LIFT NUMBER
RULE_REVCONFIDENCE NUMBER
ANTECEDENT_SUPPORT NUMBER
NUMBER_OF_ITEMS NUMBER
CONSEQUENT_SUPPORT NUMBER
CONSEQUENT_NAME VARCHAR2(4000)
CONSEQUENT_SUBNAME VARCHAR2(4000)
CONSEQUENT_VALUE VARCHAR2(4000)
ANTECEDENT SYS.XMLTYPE
Note:
All the types for three parts areVARCHAR2
. ASSO_AGGREGATES
is not applicable for 2Dimensional input format.
The following table displays rule view columns for 2Dimensional input with the descriptions of only the fields which are specific to 2D inputs.
Table 512 Rule View for 2Dimensional Input
Column Name  Description 

CONSEQUENT_SUBNAME 
For twodimensional inputs, 

Value of the consequent when setting When setting 

The antecedent is described as an itemset. The itemset contains As an example, assuming that this is not a nested table input, and the antecedent contains one item: (name
For 2Dimensional input with nested table, the subname field is filled. 
Global Detail for Association Rules
A single global detail is produced by an Association model. The following table describes a global detail returned for Association Rules model.
Table 513 Global Detail for Association Rules
Name  Description 


The number of itemsets generated 

The maximum support 

The total number of rows used in the build 

The number of association rules in the model generated 

The number of the transactions in input data 
5.4.2 Model Detail View for Frequent Itemsets
Model detail view for Frequent Itemsets describes the frequent itemsets view. Oracle recommends that you leverage model details view instead of the GET_FREQUENT_ITEMSETS
function.
The frequent itemsets view DM$VI
model_name has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2 (128)
ITEMSET_ID NUMBER
SUPPORT NUMBER
NUMBER_OF_ITEMS NUMBER
ITEMSET SYS.XMLTYPE
Table 514 Frequent Itemsets View
Column Name  Description 


A partition in a partitioned model 

Itemset identifier 

Support of the itemset 

Number of items in the itemset 

Frequent itemset The structure of the 
5.4.3 Model Detail View for Transactional Itemsets
Model detail view for Transactional Itemsets describes the transactional itemsets view. Oracle recommends that users leverage the model details views.
For the very common case of transactional data without aggregates, DM$VT
model_name view provides the itemsets information in transactional format. This view can help improve performance for some queries as compared to the view with the XML column. The transactional itemsets view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ITEMSET_ID NUMBER
ITEM_ID NUMBER
SUPPORT NUMBER
NUMBER_OF_ITEMS NUMBER
ITEM_NAME VARCHAR2(4000)
Table 515 Transactional Itemsets View
Column Name  Description 


A partition in a partitioned model 

Itemset identifier 

Item identifier 

Support of the itemset 

Number of items in the itemset 

The name of the item 
5.4.4 Model Detail View for Transactional Rule
Model detail view for Transactional Rule describes the transactional rule view and transactional itemsets view. Oracle recommends that you leverage model details views.
Transactional data without aggregates also has a transactional rule view DM$VA
model_name. This view can improve performance for some queries as compared to the view with the XML column. The transactional rule view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
RULE_ID NUMBER
ANTECEDENT_PREDICATE VARCHAR2(4000)
CONSEQUENT_PREDICATE VARCHAR2(4000)
RULE_SUPPORT NUMBER
RULE_CONFIDENCE NUMBER
RULE_LIFT NUMBER
RULE_REVCONFIDENCE NUMBER
RULE_ITEMSET_ID NUMBER
ANTECEDENT_SUPPORT NUMBER
CONSEQUENT_SUPPORT NUMBER
NUMBER_OF_ITEMS NUMBER
Table 516 Transactional Rule View
Column Name  Description 


A partition in a partitioned model 

Rule identifier 

Name of the Antecedent item. 

Name of the Consequent item 

Support of the rule 

The likelihood a transaction satisfies the rule when it contains the Antecedent. 

The degree of improvement in the prediction over random chance when the rule is satisfied 

The number of transactions in which the rule occurs divided by the number of transactions in which the consequent occurs 

Itemset identifier 

The ratio of the number of transactions that satisfy the antecedent to the total number of transactions 

The ratio of the number of transactions that satisfy the consequent to the total number of transactions 

Number of items in the rule 
5.4.5 Model Detail Views for Classification Algorithms
Model detail view for Classification algorithms describe target map view and scoring cost view which are applicable to all Classification algorithms. Oracle recommends that users leverage the model details views instead of the GET_*
function.
The target map view DM$VT
model_name describes the target distribution for Classification models. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
TARGET_VALUE NUMBER/VARCHAR2
TARGET_COUNT NUMBER
TARGET_WEIGHT NUMBER
Table 517 Target Map View
Column Name  Description 


Partition name in a partitioned model 

Target value, numerical or categorical 

Number of rows for a given 

Weight for a given 
The scoring cost view DM$VC
model_name describes the scoring cost matrix for Classification models. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ACTUAL_TARGET_VALUE NUMBER/VARCHAR2
PREDICTED_TARGET_VALUE NUMBER/VARCHAR2
COST NUMBER
Table 518 Scoring Cost View
Column Name  Description 


Partition name in a partitioned model 

A valid target value 

Predicted target value 

Associated cost for the actual and predicted target value pair 
5.4.6 Model Detail Views for CUR Matrix Decomposition
Model Detail Views for CUR matrix decomposition describe scores and ranks of attributes and rows.
CUR matrix decomposition algorithm has the following views:
Attribute importance and rank: DM$VC
model_name
Row importance and rank: DM$VR
model_name
Global statistics: DM$VG
The Attribute Importance and Rank view DM$VC
model_name has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
ATTRIBUTE_IMPORTANCE NUMBER
ATTRIBUTE_RANK NUMBER
Table 519 Attribute Importance and Rank View
Column Name  Description 


Partition name in a partitioned model 

Attribute name 

Attribute subname. The value is null for nonnested columns. 

Value of the attribute 

Attribute leverage score 

Attribute rank based on leverage score 
The view DM$VR
model_name exposes the leverage scores and ranks of all selected rows through a view. This view is created when users decide to perform row importance and the CASE_ID
column is present. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
CASE_ID Original cid data types,
including NUMBER, VARCHAR2,
DATE, TIMESTAMP,
TIMESTAMP WITH TIME ZONE,
TIMESTAMP WITH LOCAL TIME ZONE
ROW_IMPORTANCE NUMBER
ROW_RANK NUMBER
Table 520 Row Importance and Rank View
Column Name  Description 


Partition name in a partitioned model 

Case ID. The supported case ID types are the same as that supported for GLM, SVD, and ESA algorithms. 

Row leverage score 

Row rank based on leverage score 
Table 521 CUR Matrix Decomposition Statistics Information In Model Global View.
Name  Description 


Number of SVD components (SVD rank) 

Number of rows used in the model build 
5.4.7 Model Detail Views for Decision Tree
Model detail view for Decision Tree describes the split information view, node statistics view, node description view, and the cost matrix view. Oracle recommends that users leverage the model details views instead of GET_MODEL_DETAILS_XML
function.
The split information view DM$VP
model_name describes the decision tree hierarchy and the split information for each level in the Decision Tree. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
PARENT NUMBER
SPLIT_TYPE VARCHAR2
NODE NUMBER
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
OPERATOR VARCHAR2
VALUE SYS.XMLTYPE
Table 522 Split Information View
Column Name  Description 


Partition name in a partitioned model 

Node ID of the parent 

The main or surrogate split 

The node ID 

The attribute used as the splitting criterion at the parent node to produce this node. 

Split attribute subname. The value is null for nonnested columns. 

Split operator 

Value used as the splitting criterion. This is an XML element described using the For example, 
The node statistics view DM$VI
model_name describes the statistics associated with individual tree nodes. The statistics include a target histogram for the data in the node. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
NODE NUMBER
NODE_SUPPORT NUMBER
PREDICTED_TARGET_VALUE NUMBER/VARCHAR2
TARGET_VALUE NUMBER/VARCHAR2
TARGET_SUPPORT NUMBER
Table 523 Node Statistics View
Parameter  Description 


Partition name in a partitioned model 

The node ID 

Number of records in the training set that belong to the node 

Predicted Target value 

A target value seen in the training data 

The number of records that belong to the node and have the value specified in the 
Higher level node description can be found in DM$VO
model_name view. The DM$VO
model_name has the following schema:
ame Type
 
PARTITION_NAME VARCHAR2(128)
NODE NUMBER
NODE_SUPPORT NUMBER
PREDICTED_TARGET_VALUE NUMBER/VARCHAR2
PARENT NUMBER
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
OPERATOR VARCHAR2
VALUE SYS.XMLTYPE
Table 524 Node Description View
Parameter  Description 


Partition name in a partitioned model 

The node ID 

Number of records in the training set that belong to the node 

Predicted Target value 

The ID of the parent 

Specifies the attribute name 

Specifies the attribute subname 

Attribute predicate operator  a conditional operator taking the following values: IN, = , <>, < , >, <=, and >= 

Value used as the description criterion. This is an XML element described using the For example, 
The DM$VM
model_name view describes the cost matrix used by the Decision Tree build. The DM$VM
model_name view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ACTUAL_TARGET_VALUE NUMBER/VARCHAR2
PREDICTED_TARGET_VALUE NUMBER/VARCHAR2
COST NUMBER
Table 525 Cost Matrix View
Parameter  Description 


Partition name in a partitioned model 

Valid target value 

Predicted Target value 

Associated cost for the actual and predicted target value pair 
Table 526 Decision Tree Statistics Information In Model Global View
Name  Description 


The total number of rows used in the build 
5.4.8 Model Detail Views for Generalized Linear Model
Model details views for Generalized Linear Model (GLM) describes the model details view and row diagnostic view for Linear and Logistic Regression. Oracle recommends that users leverage model details views than the GET_MODEL_DETAILS_GLM
function.
The model details view DM$VD
model_name describes the final model information for both Linear Regression models and Logistic Regression models.
For Linear Regression, the view DM$VD
model_name has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
FEATURE_EXPRESSION VARCHAR2(4000)
COEFFICIENT BINARY_DOUBLE
STD_ERROR BINARY_DOUBLE
TEST_STATISTIC BINARY_DOUBLE
P_VALUE BINARY_DOUBLE
VIF BINARY_DOUBLE
STD_COEFFICIENT BINARY_DOUBLE
LOWER_COEFF_LIMIT BINARY_DOUBLE
UPPER_COEFF_LIMIT BINARY_DOUBLE
For Logistic Regression, the view DM$VD
model_name has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
TARGET_VALUE NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
FEATURE_EXPRESSION VARCHAR2(4000)
COEFFICIENT BINARY_DOUBLE
STD_ERROR BINARY_DOUBLE
TEST_STATISTIC BINARY_DOUBLE
P_VALUE BINARY_DOUBLE
STD_COEFFICIENT BINARY_DOUBLE
LOWER_COEFF_LIMIT BINARY_DOUBLE
UPPER_COEFF_LIMIT BINARY_DOUBLE
EXP_COEFFICIENT BINARY_DOUBLE
EXP_LOWER_COEFF_LIMIT BINARY_DOUBLE
EXP_UPPER_COEFF_LIMIT BINARY_DOUBLE
Table 527 Model View for Linear and Logistic Regression Models
Column Name  Description 


The name of a feature in the model 

Valid target value 

The attribute name when there is no subname, or first part of the attribute name when there is a subname. 

Nested column subname. The value is null for nonnested columns. When the nested column is numeric, the mining attribute is identified by the combination 

A unique value that can be assumed by a categorical column or nested categorical column. For categorical columns, a mining attribute is identified by a unique 

The feature name constructed by the algorithm when feature selection is enabled. If feature selection is not enabled, the feature name is simply the fullyqualified attribute name (attribute_name.attribute_subname if the attribute is in a nested column). For categorical attributes, the algorithm constructs a feature name that has the following form: fullyqualified_attribute_name.attribute_value When feature generation is enabled, a term in the model can be a single mining attribute or the product of up to 3 mining attributes. Component mining attributes can be repeated within a single term. If feature generation is not enabled or, if feature generation is enabled, but no multiple component terms are discovered by the Note: In 12c Release 2, the algorithm does not subtract the mean from numerical components. 

The estimated coefficient. 

Standard error of the coefficient estimate. 

For Linear Regression, the tvalue of the coefficient estimate. For Logistic Regression, the Wald chisquare value of the coefficient estimate. 

Probability of the 

Variance Inflation Factor. The value is zero for the intercept. For Logistic Regression, 

Standardized estimate of the coefficient. 

Lower confidence bound of the coefficient. 

Upper confidence bound of the coefficient. 

Exponentiated coefficient for Logistic Regression. For linear regression, 

Exponentiated coefficient for lower confidence bound of the coefficient for Logistic Regression. For Linear Regression, 

Exponentiated coefficient for upper confidence bound of the coefficient for Logistic Regression. For Linear Regression, 
The row diagnostic view DM$VA
model_name describes row level information for both Linear Regression models and Logistic Regression models. For Linear Regression, the view DM$VA
model_name has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
CASE_ID NUMBER/VARHCAR2, DATE, TIMESTAMP,
TIMESTAMP WITH TIME ZONE,
TIMESTAMP WITH LOCAL TIME ZONE
TARGET_VALUE BINARY_DOUBLE
PREDICTED_TARGET_VALUE BINARY_DOUBLE
Hat BINARY_DOUBLE
RESIDUAL BINARY_DOUBLE
STD_ERR_RESIDUAL BINARY_DOUBLE
STUDENTIZED_RESIDUAL BINARY_DOUBLE
PRED_RES BINARY_DOUBLE
COOKS_D BINARY_DOUBLE
Table 528 Row Diagnostic View for Linear Regression
Column Name  Description 


Partition name in a partitioned model 

Name of the case identifier 

The actual target value as taken from the input row 

The model predicted target value for the row 

The diagonal element of the n*n (n=number of rows) that the Hat matrix identifies with a specific input row. The model predictions for the input data are the product of the Hat matrix and vector of input target values. The diagonal elements (Hat values) represent the influence of the i^{th} row on the i^{th} fitted value. Large Hat values are indicators that the i^{th} row is a point of high leverage, a potential outlier. 

The difference between the predicted and actual target value for a specific input row. 

The standard error residual, sometimes called the Studentized residual, rescales the residual to have constant variance across all input rows in an effort to make the input row residuals comparable. The process multiplies the residual by square root of the row weight divided by the product of the model mean square error and 1 minus the Hat value. 

Studentized deletion residual adjusts the standard error residual for the influence of the current row. 

The predictive residual is the weighted square of the deletion residuals, computed as the row weight multiplied by the square of the residual divided by 1 minus the Hat value. 

Cook's distance is a measure of the combined impact of the i^{th} case on all of the estimated regression coefficients. 
For Logistic Regression, the view DM$VA
model_name has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
CASE_ID NUMBER/VARHCAR2, DATE, TIMESTAMP,
TIMESTAMP WITH TIME ZONE,
TIMESTAMP WITH LOCAL TIME ZONE
TARGET_VALUE NUMBER/VARCHAR2
TARGET_VALUE_PROB BINARY_DOUBLE
Hat BINARY_DOUBLE
WORKING_RESIDUAL BINARY_DOUBLE
PEARSON_RESIDUAL BINARY_DOUBLE
DEVIANCE_RESIDUAL BINARY_DOUBLE
C BINARY_DOUBLE
CBAR BINARY_DOUBLE
DIFDEV BINARY_DOUBLE
DIFCHISQ BINARY_DOUBLE
Table 529 Row Diagnostic View for Logistic Regression
Column Name  Description 


Partition name in a partitioned model 

Name of the case identifier 

The actual target value as taken from the input row 

Model estimate of the probability of the predicted target value. 

The Hat value concept from Linear Regression is extended to Logistic Regression by multiplying the Linear Regression Hat value by the variance function for Logistic Regression, the predicted probability multiplied by 1 minus the predicted probability. 

The working residual is the residual of the working response. The working response is the response on the linearized scale. For Logistic Regression it has the form: the i^{th} row residual divided by the variance of the i^{th} row prediction. The variance of the prediction is the predicted probability multiplied by 1 minus the predicted probability.


The Pearson residual is a rescaled version of the working residual, accounting for the weight. For Logistic Regression, the Pearson residual multiplies the residual by a factor that is computed as square root of the weight divided by the variance of the predicted probability for the i^{th} row.


The 

Measures the overall change in the fitted logits due to the deletion of the i^{th} observation for all points including the one deleted (the i^{th} point). It is computed as the square of the Pearson residual multiplied by the Hat value divided by the square of 1 minus the Hat value. Confidence interval displacement diagnostics that provides scalar measure of the influence of individual observations. 

C and CBAR are extensions of Cooks’ distance for Logistic Regression. CBAR measures the overall change in the fitted logits due to the deletion of the i^{th} observation for all points excluding the one deleted (the i^{th} point). It is computed as the square of the Pearson residual multiplied by the Hat value divided by (1 minus the Hat value)
Confidence interval displacement diagnostic which measures the influence of deleting an individual observation. 

A statistic that measures the change in deviance that occurs when an observation is deleted from the input. It is computed as the square of the deviance residual plus 

A statistic that measures the change in the Pearson chisquare statistic that occurs when an observation is deleted from the input. It is computed as 
Global Details for GLM: Linear Regression
The following table describes global details returned by a Linear Regression model.
Table 530 Global Details for Linear Regression
Name  Description 


Adjusted RSquare 

Akaike's information criterion 

Coefficient of variation 

Indicates whether the model build process has converged to specified tolerance. The following are the possible values:


Corrected total degrees of freedom 

Corrected total sum of squares 

Dependent mean 

Error degrees of freedom 

Error mean square 

Error sum of squares 

Model F value statistic 

Estimated mean square error of the prediction, assuming multivariate normality 

Hocking Sp statistic 

Tracks the number of SGD iterations. Applicable only when the solver is SGD. 

JP statistic (the final prediction error) 

Model degrees of freedom 

Model F value probability 

Model mean square error 

Model sum of square errors 

Number of parameters (the number of coefficients, including the intercept) 

Number of rows 

RSquare 

The number of predictors excluded from the model due to multicollinearity 

Root mean square error 

Schwarz's Bayesian information criterion 
Global Details for GLM: Logistic Regression
The following table returns global details returned by a Logistic Regression model.
Table 531 Global Details for Logistic Regression
Name  Description 


Akaike's criterion for the fit of the baseline, interceptonly, model 

Akaike's criterion for the fit of the intercept and the covariates (predictors) mode 

Indicates whether the model build process has converged to specified tolerance. The following are the possible values:


Dependent mean 

Tracks the number of SGD iterations (number of IRLS iterations). Applicable only when the solver is SGD. 

Likelihood ratio degrees of freedom 

Likelihood ratio chisquare value 

Likelihood ratio chisquare probability value 

2 log likelihood of the baseline, interceptonly, model 

2 log likelihood of the model 

Number of parameters (the number of coefficients, including the intercept) 

Number of rows 

Percent of correct predictions 

Percent of incorrectly predicted rows 

Percent of cases where the estimated probabilities are equal for both target classes 

Pseudo Rsquare Cox and Snell 

Pseudo Rsquare Nagelkerke 

The number of predictors excluded from the model due to multicollinearity 

Schwarz's Criterion for the fit of the baseline, interceptonly, model 

Schwarz's Criterion for the fit of the intercept and the covariates (predictors) model 
Note:

When Ridge Regression is enabled, fewer global details are returned. For information about ridge, see Oracle Data Mining Concepts.

When the value is
NULL
for a partitioned model, an exception is thrown. When the value is not null, it must contain the desired partition name.
5.4.9 Model Detail Views for Naive Bayes
Model Detail Views for Naive Bayes describes prior view and result view. Oracle recommends that users leverage the model details views instead of the GET_MODEL_DETAILS_NB
function.
The prior view DM$VP
model_name describes the priors of the targets for Naïve Bayes. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
TARGET_NAME VARCHAR2(128)
TARGET_VALUE NUMBER/VARCHAR2
PRIOR_PROBABILITY BINARY_DOUBLE
COUNT NUMBER
Table 532 Prior View for Naive Bayes
Column Name  Description 


The name of a feature in the model 

Name of the target column 

Target value, numerical or categorical 

Prior probability for a given 

Number of rows for a given 
The Naïve Bayes result view DM$VV
model_view describes the conditional probabilities of the Naïve Bayes model. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
TARGET_NAME VARCHAR2(128)
TARGET_VALUE NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
CONDITIONAL_PROBABILITY BINARY_DOUBLE
COUNT NUMBER
Table 533 Result View for Naive Bayes
Column Name  Description 


The name of a feature in the model 

Name of the target column 

Target value, numerical or categorical 

Column name 

Nested column subname. The value is null for nonnested columns. 

Mining attribute value for the column 

Conditional probability of a mining attribute for a given target 

Number of rows for a given mining attribute and a given target 
Table 534 Naive Bayes Statistics Information In Model Global View
Name  Description 


The total number of rows used in the build 
5.4.10 Model Detail Views for Neural Network
Model Detail Views for Neural Network describes the weights of the neurons: input layer and hidden layers. Oracle recommends that users leverage the model details views.
Neural Network algorithm has the following views:
Weights: DM$VA
model_name
The view DM$VA
model_name has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
LAYER NUMBER
IDX_FROM NUMBER
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
IDX_TO NUMBER
TARGET_VALUE NUMBER/VARCHAR2
WEIGHT BINARY_DOUBLE
Table 535 Weights View
Column Name  Description 


Partition name in a partitioned model 

Layer ID, 0 as an input layer 

Node index that the weight connects from (attribute id for input layer) 

Attribute name (only for the input layer) 

Attribute subname. The value is null for nonnested columns. 

Categorical attribute value 

Node index that the weights connects to 

Target value. The value is null for regression. 

Value of the weight 
The view DM$VG
model_name is a preexisting view. The following namevalue pairs are added to the view.
Table 536 Neural Networks Statistics Information In Model Global View
Name  Description 


Indicates whether the model build process has converged to specified tolerance. The following are the possible values:


Number of iterations 

Loss function value (if it is with 

Number of rows in the model (or partitioned model) 
5.4.11 Model Detail Views for Random Forest
Model Detail Views for Random Forest describes variable importance measures and statistics in global view. Oracle recommends that users leverage the model details views.
Random Forest algorithm has the following statistics views:

Variable importance statistics
DM$VA
model_name 
Random Forest statistics in model global view
DM$VG
model_name
One of the important outputs from the Random Forest model build is a ranking of attributes based on their relative importance. This is measured using Mean Decrease Gini. The view DM$VA
model_name has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(128)
ATTRIBUTE_IMPORTANCE BINARY_DOUBLE
Table 537 Variable Importance Model View
Column Name  Description 


Partition name. The value is null for models which are not partitioned. 

Column name 

Nested column subname. The value is null for nonnested columns. 

Measure of importance for an attribute in the forest (mean Decrease Gini value) 
The view DM$VG
model_name is a preexisting view. The following namevalue pairs are added to the view.
Table 538 Random Forest Statistics Information In Model Global View
Name  Description 


Average depth of the trees in the forest 

Average number of nodes per tree 

Maximum depth of the trees in the forest 

Maximum number of nodes per tree 

Minimum depth of the trees in the forest 

Minimum number of nodes per tree 

The total number of rows used in the build 
5.4.12 Model Detail View for Support Vector Machine
Model Detail View for Support Vector Machine describes linear coefficient view. Oracle recommends that users leverage the model details views instead of the GET_MODEL_DETAILS_SVM
function.
The linear coefficient view DM$VL
model_name describes the coefficients of a linear SVM algorithm. The target_value field in the view is present only for Classification and has the type of the target. Regression models do not have a target_value field.
The reversed_coefficient field shows the value of the coefficient after reversing the automatic data preparation transformations. If data preparation is disabled, then coefficient and reversed_coefficient have the same value. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
TARGET_VALUE NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
COEFFICIENT BINARY_DOUBLE
REVERSED_COEFFICIENT BINARY_DOUBLE
Table 539 Linear Coefficient View for Support Vector Machine
Column Name  Description 


Partition name in a partitioned model 

Target value, numerical or categorical 

Column name 

Nested column subname. The value is null for nonnested columns. 

Value of a categorical attribute 

Projection coefficient value 

Coefficient transformed on the original scale 
Table 540 Support Vector Statistics Information In Model Global View
Name  Description 


Indicates whether the model build process has converged to specified tolerance:


Number of iterations performed during build 

Number of rows used for the build 

Number of rows removed due to 0 norm. This applies to oneclass linear models only. 
5.4.13 Model Detail Views for Clustering Algorithms
Oracle Data Mining supports these clustering algorithms: Expectation Maximization, kMeans, and Orthogonal Partitioning Clustering (OCluster).
All clustering algorithms share the following views:

Cluster description
DM$VD
model_name 
Attribute statistics
DM$VA
model_name 
Histogram statistics
DM$VH
model_name 
Rule statistics
DM$VR
model_name
The cluster description view DM$VD
model_name describes cluster level information about a clustering model. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
CLUSTER_ID NUMBER
CLUSTER_NAME NUMBER/VARCHAR2
RECORD_COUNT NUMBER
PARENT NUMBER
TREE_LEVEL NUMBER
LEFT_CHILD_ID NUMBER
RIGHT_CHILD_ID NUMBER
Table 541 Cluster Description View for Clustering Algorithm
Column Name  Description 


Partition name in a partitioned model 

The ID of a cluster in the model 

Specifies the label of the cluster 

Specifies the number of records 

The ID of the parent 

Specifies the number of splits from the root 

The ID of the child cluster on the left side of the split 

The ID of the child cluster on the right side of the split 
The attribute view DM$VA
model_name describes attribute level information about a Clustering model. The values of the mean, variance, and mode for a particular cluster can be obtained from this view. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
CLUSTER_ID NUMBER
CLUSTER_NAME NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
MEAN BINARY_DOUBLE
VARIANCE BINARY_DOUBLE
MODE_VALUE VARCHAR2(4000)
Table 542 Attribute View for Clustering Algorithm
Column Name  Description 


A partition in a partitioned model 

The ID of a cluster in the model 

Specifies the label of the cluster 

Specifies the attribute name 

Specifies the attribute subname 

The field returns the average value of a numeric attribute 

The variance of a numeric attribute 

The mode is the most frequent value of a categorical attribute 
The histogram view DM$VH
model_name describes histogram level information about a Clustering model. The bin information as well as bin counts can be obtained from this view. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
CLUSTER_ID NUMBER
CLUSTER_NAME NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
BIN_ID NUMBER
LOWER_BIN_BOUNDARY BINARY_DOUBLE
UPPER_BIN_BOUNDARY BINARY_DOUBLE
ATTRIBUTE_VALUE VARCHAR2(4000)
COUNT NUMBER
Table 543 Histogram View for Clustering Algorithm
Column Name  Description 


A partition in a partitioned model 

The ID of a cluster in the model 

Specifies the label of the cluster 

Specifies the attribute name 

Specifies the attribute subname 

Bin ID 

Numeric lower bin boundary 

Numeric upper bin boundary 

Categorical attribute value 

Histogram count 
The rule view DM$VR
model_name describes the rule level information about a Clustering model. The information is provided at attribute predicate level. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
CLUSTER_ID NUMBER
CLUSTER_NAME NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
OPERATOR VARCHAR2(2)
NUMERIC_VALUE NUMBER
ATTRIBUTE_VALUE VARCHAR2(4000)
SUPPORT NUMBER
CONFIDENCE BINARY_DOUBLE
RULE_SUPPORT NUMBER
RULE_CONFIDENCE BINARY_DOUBLE
Table 544 Rule View for Clustering Algorithm
Column Name  Description 


A partition in a partitioned model 

The ID of a cluster in the model 

Specifies the label of the cluster 

Specifies the attribute name 

Specifies the attribute subname 

Attribute predicate operator  a conditional operator taking the following values: IN, = , <>, < , >, <=, and >= 

Numeric lower bin boundary 

Categorical attribute value 

Attribute predicate support 

Attribute predicate confidence 

Rule level support 

Rule level confidence 
5.4.14 Model Detail Views for Expectation Maximization
Model detail views for Expectation Maximization (EM) describes the differences in the views for EM against those of Clustering views. Oracle recommends that user leverage the model details views instead of the GET_MODEL_DETAILS_EM
function.
The following views are the differences in the views for Expectation Maximization against Clustering views. For an overview of the different Clustering views, refer to "Model Detail Views for Clustering Algorithms".
The component view DM$VO
model_name describes the EM components. The component view contains information about their prior probabilities and what cluster they map to. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
COMPONENT_ID NUMBER
CLUSTER_ID NUMBER
PRIOR_PROBABILITY BINARY_DOUBLE
Table 545 Component View
Column Name  Description 


Partition name in a partitioned model 

Unique identifier of a component 

The ID of a cluster in the model 

Component prior probability 
The mean and variance component view DM$VM
model_name provides information about the mean and variance parameters for the attributes by Gaussian distribution models. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
COMPONENT_ID NUMBER
ATTRIBUTE_NAME VARCHAR2(4000)
MEAN BINARY_DOUBLE
VARIANCE BINARY_DOUBLE
The frequency component view DM$VF
model_name provides information about the parameters of the multivalued Bernoulli distributions used by the EM model. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
COMPONENT_ID NUMBER
ATTRIBUTE_NAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
FREQUENCY BINARY_DOUBLE
Table 546 Frequency Component View
Column Name  Description 


Partition name in a partitioned model 

Unique identifier of a component 

Column name 

Categorical attribute value 

The frequency of the multivalued Bernoulli distribution for the attribute/value combination specified by 
For 2Dimensional columns, EM provides an attribute ranking similar to that of Attribute Importance. This ranking is based on a rankweighted average over Kullback–Leibler divergence computed for pairs of columns. This unsupervised Attribute Importance is shown in the DM$VI
model_name view and has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_IMPORTANCE_VALUE BINARY_DOUBLE
ATTRIBUTE_RANK NUMBER
Table 547 2–Dimensional Attribute Ranking for Expectation Maximization
Column Name  Description 


Partition name in a partitioned model 

Column name 

Importance value 

An attribute rank based on the importance value 
The pairwise
Kullback–Leibler divergence is reported in the DM$VB
model_name view. This metric evaluates how much the observed joint distribution of two attributes diverges from the expected distribution under the assumption of independence. That is, the higher the value, the more dependent the two attributes are. The dependency value is scaled based on the size of the grid used for each pairwise computation. That ensures that all values fall within the [0; 1] range and are comparable. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ATTRIBUTE_NAME_1 VARCHAR2(128)
ATTRIBUTE_NAME_2 VARCHAR2(128)
DEPENDENCY BINARY_DOUBLE
Table 548 KullbackLeibler Divergence for Expectation Maximization
Column Name  Description 


Partition name in a partitioned model 

Name of an attribute 1 

Name of an attribute 2 

Scaled pairwise KullbackLeibler divergence 
The projection table DM$VP
model_name shows the coefficients used by random projections to map nested columns to a lower dimensional space. The view has rows only when nested or text data is present in the build data. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
FEATURE_NAME VARCHAR2(4000)
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
COEFFICIENT NUMBER
Table 549 Projection table for Expectation Maximization
Column Name  Description 


Partition name in a partitioned model 

Name of feature 

Column name 

Nested column subname. The value is null for nonnested columns. 

Categorical attribute value 

Projection coefficient. The representation is sparse; only the nonzero coefficients are returned. 
Global Details for Expectation Maximization
The following table describes global details for Expectation Maximization.
Table 550 Global Details for Expectation Maximization
Name  Description 


Indicates whether the model build process has converged to specified tolerance. The possible values are:


Loglikelihood on the build data 

Number of components produced by the model 

Number of clusters produced by the model 

Number of rows used in the build 

The random seed value used for the model build 

The number of empty components excluded from the model 
Related Topics
5.4.15 Model Detail Views for kMeans
Model detail views for kMeans (KM) describes cluster description view and scoring view. Oracle recommends that you leverage model details view instead of GET_MODEL_DETAILS_KM
function.
This section describes the differences in the views for kMeans against the Clustering views. For an overview of the different views, refer to "Model Detail Views for Clustering Algorithms". For kMeans, the cluster description view DM$VD
model_name has an additional column:
Name Type
 
DISPERSION BINARY_DOUBLE
Table 551 Cluster Description for kMeans
Column Name  Description 


A measure used to quantify whether a set of observed occurrences are dispersed compared to a standard statistical model. 
The scoring view DM$VC
model_name describes the centroid of each leaf clusters:
Name Type
 
PARTITION_NAME VARCHAR2(128)
CLUSTER_ID NUMBER
CLUSTER_NAME NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
VALUE BINARY_DOUBLE
Table 552 Scoring View for kMeans
Column Name  Description 


Partition name in a partitioned model 

The ID of a cluster in the model 

Specifies the label of the cluster 

Column name 

Nested column subname. The value is null for nonnested columns. 

Categorical attribute value 

Specifies the centroid value 
Table 553 k–Means Statistics Information In Model Global View
Name  Description 


Indicates whether the model build process has converged to specified tolerance. The following are the possible values:


Number of rows used in the build 

Number of rows removed due to 0 norm. This applies only to models using cosine distance. 
Related Topics
5.4.16 Model Detail Views for OCluster
Model Detail Views for OCluster describes the statistics views. Oracle recommends that user leverage the model details views instead of the GET_MODEL_DETAILS_OC
function.
The following are the differences in the views for OCluster against Clustering views. For an overview of the different clustering views, refer to "Model Detail Views for Clustering Algorithms". The OC algorithm uses the same descriptive statistics views as Expectation Maximization (EM) and kMeans (KM). The following are the statistics views:

Cluster description
DM$VD
model_name 
Attribute statistics
DM$VA
model_name 
Rule statistics
DM$VR
model_name 
Histogram statistics
DM$VH
model_name
The Cluster description view DM$VD
model_name describes the OCluster components. The cluster description view has additional fields that specify the split predicate. The view has the following schema:
Name Type
 
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
OPERATOR VARCHAR2(2)
VALUE SYS.XMLTYPE
Table 554 Description View
Column Name  Description 


Column name 

Nested column subname. The value is null for nonnested columns. 

Split operator 

List of split values 
SYS.XMLTYPE
is as follows:<Element>splitval1</Element>
The OC algorithm uses a histogram view DM$VH
model_name with a different schema than EM and kMeans (KM). The view has the following schema:
Name Type
 
PARTITON_NAME VARCHAR2(128)
CLUSTER_ID NUMBER
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
BIN_ID NUMBER
LABEL VARCHAR2(4000)
COUNT NUMBER
Table 555 Histogram Component View
Column Name  Description 


Partition name in a partitioned model 

Unique identifier of a component 

Column name 

Nested column subname. The value is null for nonnested columns. 

Unique identifier 

Bin label 

Bin histogram count 
Table 556 OCluster Statistics Information In Model Global View
Name  Description 


The total number of rows used in the build 
Related Topics
5.4.17 Model Detail Views for Explicit Semantic Analysis
Model Detail Views for Explicit Semantic Analysis (ESA) describes attribute statistics view and feature view. Oracle recommends that users leverage the model details view.
ESA algorithm has the following views:

Explicit Semantic Analysis Matrix
DM$VA
model_name: This view has different schemas for Feature Extraction and Classification. For Feature Extraction, this view contains model attribute coefficients per feature. For Classification, this view contains model attribute coefficients per target class. 
Explicit Semantic Analysis Features
DM$VF
model_name: This view is applicable for only Feature Extraction.
The view DM$VA
model_name has the following schema for Feature Extraction:
PARTITION_NAME VARCHAR2(128)
FEATURE_ID NUMBER/VARHCAR2, DATE, TIMESTAMP,
TIMESTAMP WITH TIME ZONE,
TIMESTAMP WITH LOCAL TIME ZONE
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
COEFFICIENT BINARY_DOUBLE
Table 557 Explicit Semantic Analysis Matrix for Feature Extraction
Column Name  Description 


Partition name in a partitioned model 

Unique identifier of a feature as it appears in the training data 

Column name 

Nested column subname. The value is null for nonnested columns. 

Categorical attribute value 

A measure of the weight of the attribute with respect to the feature 
The DM$VA
model_name view comprises attribute coefficients for all target classes.
The view DM$VA
model_name has the following schema for Classification:
Name Type
 
PARTITION_NAME VARCHAR2(128)
TARGET_VALUE NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
COEFFICIENT BINARY_DOUBLE
Table 558 Explicit Semantic Analysis Matrix for Classification
Column Name  Description 


Partition name in a partitioned model 

Value of the target 

Column name 

Nested column subname. The value is null for nonnested columns. 

Categorical attribute value 

A measure of the weight of the attribute with respect to the feature 
The view DM$VF
model_name has a unique row for every feature in one view. This feature is helpful if the model was prebuilt and the source training data are not available. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
FEATURE_ID NUMBER/VARHCAR2, DATE, TIMESTAMP,
TIMESTAMP WITH TIME ZONE,
TIMESTAMP WITH LOCAL TIME ZONE
Table 559 Explicit Semantic Analysis Features for Explicit Semantic Analysis
Column Name  Description 


Partition name in a partitioned model 

Unique identifier of a feature as it appears in the training data 
Table 560 Explicit Semantic Analysis Statistics Information In Model Global View
Name  Description 


The total number of input rows 

Number of rows removed by filters 
5.4.18 Model Detail Views for NonNegative Matrix Factorization
Model detail views for NonNegative Matrix Factorization (NMF) describes encoding H matrix view and H inverse matrix view. Oracle recommends that users leverage the model details views instead of the GET_MODEL_DETAILS_NMF
function.
The NMF algorithm has two matrix content views:

Encoding (H) matrix
DM$VE
model_name 
H inverse matrix
DM$VI
model_name
The view DM$VE
model_name describes the encoding (H) matrix of an NMF model. The FEATURE_NAME
column type may be either NUMBER
or VARCHAR2
. The view has the following schema definition.
Name Type
 
PARTITION_NAME VARCHAR2(128)
FEATURE_ID NUMBER
FEATURE_NAME NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
COEFFICIENT BINARY_DOUBLE
Table 561 Encoding H Matrix View for NonNegative Matrix Factorization
Column Name  Description 


Partition name in a partitioned model 

The ID of a feature in the model 

The name of a feature in the model 

Column name 

Nested column subname. The value is null for nonnested columns. 

Specifies the value of attribute 

The attribute encoding that represents its contribution to the feature 
The view DM$VI
model_view describes the inverse H matrix of an NMF model. The FEATURE_NAME
column type may be either NUMBER
or VARCHAR2
. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
FEATURE_ID NUMBER
FEATURE_NAME NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
COEFFICIENT BINARY_DOUBLE
Table 562 Inverse H Matrix View for NonNegative Matrix Factorization
Column Name  Description 


Partition name in a partitioned model 

The ID of a feature in the model 

The name of a feature in the model 

Column name 

Nested column subname. The value is null for nonnested columns. 

Specifies the value of attribute 

The attribute encoding that represents its contribution to the feature 
Table 563 NonNegative Matrix Factorization Statistics Information In Model Global View
Name  Description 


Convergence error 

Indicates whether the model build process has converged to specified tolerance. The following are the possible values:


Number of iterations performed during build 

Number of rows used in the build input dataset 

Number of rows used by the build 
5.4.19 Model Detail Views for Singular Value Decomposition
Model detail views for Singular Value Decomposition (SVD) describes S Matrix view, rightsingular vectors view, and leftsingular vector view. Oracle recommends that users leverage the model details views instead of the GET_MODEL_DETAILS_SVD
function.
The DM$VE
model_name view leverages the fact that each singular value in the SVD model has a corresponding principal component in the associated Principal Components Analysis (PCA) model to relate a common set of information for both classes of models. For a SVD model, it describes the content of the S matrix. When PCA scoring is selected as a build setting, the variance and percentage cumulative variance for the corresponding principal components are shown as well. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
FEATURE_ID NUMBER
FEATURE_NAME NUMBER/VARCHAR2
VALUE BINARY_DOUBLE
VARIANCE BINARY_DOUBLE
PCT_CUM_VARIANCE BINARY_DOUBLE
Table 564 S Matrix View
Column Name  Description 


Partition name in a partitioned model 

The ID of a feature in the model 

The name of a feature in the model 

The matrix entry value 

The variance explained by a component. This column is only present for SVD models with setting This column is nonnull only if the build data is centered, either manually or because of the following setting: 

The percent cumulative variance explained by the components thus far. The components are ranked by the explained variance in descending order. This column is only present for SVD models with setting This column is nonnull only if the build data is centered, either manually or because of the following setting: 
The SVD DM$VV
model_view describes the rightsingular vectors of SVD model. For a PCA model it describes the principal components (eigenvectors). The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
FEATURE_ID NUMBER
FEATURE_NAME NUMBER/VARCHAR2
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_VALUE VARCHAR2(4000)
VALUE BINARY_DOUBLE
Table 565 Rightsingular Vectors of Singular Value Decomposition
Column Name  Description 


Partition name in a partitioned model 

The ID of a feature in the model 

The name of a feature in the model 

Column name 

Nested column subname. The value is null for nonnested columns. 

Categorical attribute value. For numerical attributes, 

The matrix entry value 
DM$VU
model_name describes the leftsingular vectors of a SVD model. For a PCA model, it describes the projection of the data in the principal components. This view does not exist unless the settings dbms_data_mining.svds_u_matrix_output
is set to dbms_data_mining.svds_u_matrix_enable
. The view has the following schema:Name Type
 
PARTITION_NAME VARCHAR2(128)
CASE_ID NUMBER/VARHCAR2, DATE, TIMESTAMP,
TIMESTAMP WITH TIME ZONE,
TIMESTAMP WITH LOCAL TIME ZONE
FEATURE_ID NUMBER
FEATURE_NAME NUMBER/VARCHAR2
VALUE BINARY_DOUBLE
Table 566 Leftsingular Vectors of Singular Value Decomposition or Projection Data in Principal Components
Column Name  Description 


Partition name in a partitioned model 

Unique identifier of the row in the build data described by the U matrix projection. 

The ID of a feature in the model 

The name of a feature in the model 

The matrix entry value 
Global Details for Singular Value Decomposition
The following table describes a global detail for Singular Value Decomposition.
Table 567 Global Details for Singular Value Decomposition
Name  Description 


Number of features (components) produced by the model 

The total number of rows used in the build 

Suggested cutoff that indicates how many of the top computed features capture most of the variance in the model. Using only the features below this cutoff would be a reasonable strategy for dimensionality reduction. 
Related Topics
5.4.20 Model Detail View for Minimum Description Length
Model detail view for Minimum Description Length (for calculating Attribute Importance) describes Attribute Importance view. Oracle recommends that users leverage the model details views instead of the GET_MODEL_DETAILS_AI
function.
The Attribute Importance view DM$VA
model_name describes the Attribute Importance as well as the Attribute Importance rank. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
ATTRIBUTE_IMPORTANCE_VALUE BINARY_DOUBLE
ATTRIBUTE_RANK NUMBER
Table 568 Attribute Importance View for Minimum Description Length
Column Name  Description 


Partition name in a partitioned model 

Column name 

Nested column subname. The value is null for nonnested columns. 

Importance value 

Rank based on importance 
Table 569 Minimum Description Length Statistics Information In Model Global View
Name  Description 


The total number of rows used in the build 
5.4.21 Model Detail View for Binning
The binning view DM$VB
describes the bin boundaries used in the automatic data preparation.
The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
BIN_ID NUMBER
LOWER_BIN_BOUNDARY BINARY_DOUBLE
UPPER_BIN_BOUNDARY BINARY_DOUBLE
ATTRIBUTE_VALUE VARCHAR2(4000)
Table 570 Model Details View for Binning
Column Name  Description 


Partition name in a partitioned model 

Specifies the attribute name 

Specifies the attribute subname 

Bin ID (or bin identifier) 

Numeric lower bin boundary 

Numeric upper bin boundary 

Categorical value 
5.4.22 Model Detail Views for Global Information
Model detail views for Global Information describes global statistics view, alert view, and computed settings view. Oracle recommends that users leverage the model details views instead of GET_MODEL_DETAILS_GLOBAL
function.
The global statistics view DM$VG
model_name describes global statistics related to the model build. Examples include the number of rows used in the build, the convergence status, and the model quality metrics. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
NAME VARCHAR2(30)
NUMERIC_VALUE NUMBER
STRING_VALUE VARCHAR2(4000)
Table 571 Global Statistics View
Column Name  Description 


Partition name in a partitioned model 

Name of the statistic 

Numeric value of the statistic 

Categorical value of the statistic 
The alert view DM$VW
model_name lists alerts issued during the model build. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ERROR_NUMBER BINARY_DOUBLE
ERROR_TEXT VARCHAR2(4000)
Table 572 Alert View
Column Name  Description 


Partition name in a partitioned model 

Error number (valid when event is Error) 

Error message 
The computed settings view DM$VS
model_name lists the algorithm computed settings. The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
SETTING_NAME VARCHAR2(30)
SETTING_VALUE VARCHAR2(4000)
Table 573 Computed Settings View
Column Name  Description 


Partition name in a partitioned model 

Name of the setting 

Value of the setting 
5.4.23 Model Detail View for Normalization and Missing Value Handling
The Normalization and Missing Value Handling View DM$VN
describes the normalization parameters used in Automatic Data Preparation (ADP) and the missing value replacement when a NULL
value is encountered. Missing value replacement applies only to the twodimensional columns and does not apply to the nested columns.
The view has the following schema:
Name Type
 
PARTITION_NAME VARCHAR2(128)
ATTRIBUTE_NAME VARCHAR2(128)
ATTRIBUTE_SUBNAME VARCHAR2(4000)
NUMERIC_MISSING_VALUE BINARY_DOUBLE
CATEGORICAL_MISSING_VALUE VARCHAR2(4000)
NORMALIZATION_SHIFT BINARY_DOUBLE
NORMALIZATION_SCALE BINARY_DOUBLE
Table 574 Normalization and Missing Value Handling View
Column Name  Description 


A partition in a partitioned model 

Column name 

Nested column subname. The value is null for nonnested columns. 

Numeric missing value replacement 

Categorical missing value replacement 

Normalization shift value 

Normalization scale value 
5.4.24 Model Detail Views for Exponential Smoothing Models
Model Detail Views for Exponential Smoothing Model (ESM) describes the views for model output and global information. Oracle recommends that users leverage the model details views.
Exponential Smoothing Model algorithm has the following views:
Model output: DM$VP
model_name
Model global information: DM$VG
model_name
Model output: This view gives the result of ESM model. The output has a set of records such as partition, CASE_ID
, value, prediction, lower, upper, and so on and ordered by partition and CASE_ID
(time). Each partition has a separate smoothing model. For a given partition, for each time (CASE_ID
) point that the input time series covers, the value is the observed or accumulated value at the time point, and the prediction is the onestepahead forecast at that time point. For each time point (future prediction) beyond the range of input time series, the value is NULL
, and the prediction is the model forecast for that time point. Lower and upper are the lower bound and upper bound of the user specified confidence interval for the prediction.
Model global Information: This view gives the global information of the model along with the estimated smoothing constants, the estimated initial state, and global diagnostic measures.
Table 575 Exponential Smoothing Model Statistics Information In Model Global View
Name  Description 


Negative loglikelihood of model 

Smoothing constant 

Akaike information criterion 

Corrected Akaike information criterion 

Average mean square error over userspecified time window 

Trend smoothing constant 

Bayesian information criterion 

Seasonal smoothing constant 

Model estimate of value one time interval prior to start of observed series 

Model estimate of seasonal effect for season i one time interval prior to start of observed series 

Model estimate of trend one time interval prior to start of observed series 

Model mean absolute error 

Model mean square error 

Damping parameter 

Model standard error 

Model standard deviation of residuals 