Explains how to create data mining models and query model details.
Explains the preparation steps before creating a model.
Models are database schema objects that perform data mining. The DBMS_DATA_MINING PL/SQL package is the API for creating, configuring, evaluating, and querying mining models (model details).
Before you create a model, you must decide what you want the model to do. You must identify the training data and determine if transformations are required. You can specify model settings to influence the behavior of the model behavior. The preparation steps are summarized in the following table.
Table 25-1 Preparation for Creating a Mining Model
| Preparation Step | Description |
|---|---|
|
See "Choosing the Algorithm" |
|
|
See "Preparing the Data" |
|
|
See " Transforming the Data" |
|
Related Topics
The CREATE_MODEL procedure in the DBMS_DATA_MINING package uses the specified data to create a mining model with the specified name and mining function. The model can be created with configuration settings and user-specified transformations.
PROCEDURE CREATE_MODEL(
model_name IN VARCHAR2,
mining_function IN VARCHAR2,
data_table_name IN VARCHAR2,
case_id_column_name IN VARCHAR2,
target_column_name IN VARCHAR2 DEFAULT NULL,
settings_table_name IN VARCHAR2 DEFAULT NULL,
data_schema_name IN VARCHAR2 DEFAULT NULL,
settings_schema_name IN VARCHAR2 DEFAULT NULL,
xform_list IN TRANSFORM_LIST DEFAULT NULL);
Explains about providing mining function to CREATE_MODEL.
The mining function is a required argument to the CREATE_MODEL procedure. A data mining function specifies a class of problems that can be modeled and solved.
Data mining functions implement either supervised or unsupervised learning. Supervised learning uses a set of independent attributes to predict the value of a dependent attribute or target. Unsupervised learning does not distinguish between dependent and independent attributes. Supervised functions are predictive. Unsupervised functions are descriptive.
Note:
In data mining terminology, a function is a general type of problem to be solved by a given approach to data mining. In SQL language terminology, a function is an operator that returns a value.
In Oracle Data Mining documentation, the term function, or mining function refers to a data mining function; the term SQL function or SQL Data Mining function refers to a SQL function for scoring (applying data mining models).
You can specify any of the values in the following table for the mining_function parameter to CREATE_MODEL.
Table 25-2 Mining Model Functions
Related Topics
Learn about providing the algorithm settings for a model.
The ALGO_NAME setting specifies the algorithm for a model. If you use the default algorithm for the mining function, or if there is only one algorithm available for the mining function, you do not need to specify the ALGO_NAME setting. Instructions for specifying model settings are in "Specifying Model Settings".
Table 25-3 Data Mining Algorithms
Related Topics
The following are the ways to create a transformation list:
The STACK interface in DBMS_DATA_MINING_TRANSFORM.
The STACK interface offers a set of pre-defined transformations that you can apply to an attribute or to a group of attributes. For example, you can specify supervised binning for all categorical attributes.
The SET_TRANSFORM procedure in DBMS_DATA_MINING_TRANSFORM.
The SET_TRANSFORM procedure applies a specified SQL expression to a specified attribute. For example, the following statement appends a transformation instruction for country_id to a list of transformations called my_xforms. The transformation instruction divides country_id by 10 before algorithmic processing begins. The reverse transformation multiplies country_id by 10.
dbms_data_mining_transform.SET_TRANSFORM (my_xforms,
'country_id', NULL, 'country_id/10', 'country_id*10');
The reverse transformation is applied in the model details. If country_id is the target of a supervised model, the reverse transformation is also applied to the scored target.
Understand the interaction between transformation list and Automatic Data Preparation (ADP).
The transformation list argument to CREATE_MODEL interacts with the PREP_AUTO setting, which controls ADP:
When ADP is on and you specify a transformation list, your transformations are applied with the automatic transformations and embedded in the model. The transformations that you specify are executed before the automatic transformations.
When ADP is off and you specify a transformation list, your transformations are applied and embedded in the model, but no system-generated transformations are performed.
When ADP is on and you do not specify a transformation list, the system-generated transformations are applied and embedded in the model.
When ADP is off and you do not specify a transformation list, no transformations are embedded in the model; you must separately prepare the data sets you use for building, testing, and scoring the model.
Oracle Data Mining supports building of a persistent Oracle Data Mining partitioned model. A partitioned model organizes and represents multiple models as partitions in a single model entity, enabling a user to easily build and manage models tailored to independent slices of data.
Persistent means that the partitioned model has an on-disk representation. The product manages the organization of the partitioned model and simplifies the process of scoring the partitioned model. You must include the partition columns as part of the USING clause when scoring.
ALL_MINING_MODEL_PARTITIONS view.
See Also:
Oracle Data Mining User’s Guide
To build a Partitioned Model, Oracle Data Mining requires a partitioning key. The partition key is set through a build setting in the settings table.
The partitioning key is a comma-separated list of one or more columns (up to 16) from the input data set. The partitioning key horizontally slices the input data based on discrete values of the partitioning key. That is, partitioning is performed as list values as opposed to range partitioning against a continuous value. The partitioning key supports only columns of the data type NUMBER and VARCHAR2.
During the build process the input data set is partitioned based on the distinct values of the specified key. Each data slice (unique key value) results in its own model partition. This resultant model partition is not separate and is not visible to you as a standalone model. The default value of the maximum number of partitions for partitioned models is 1000 partitions. You can also set a different maximum partitions value. If the number of partitions in the input data set exceed the defined maximum, Oracle Data Mining throws an exception.
The Partitioned Model organizes features common to all partitions and the partition specific features. The common features consist of the following metadata:
The model name
The mining function
The mining algorithm
A super set of all mining model attributes referenced by all partitions (signature)
A common set of user-defined column transformations
Any user-specified or default build settings that are interpreted as global. For example, the Auto Data Preparation (ADP) setting
Oracle Data Mining supports dropping a single model partition for a given partition name.
If only a single partition remains, you cannot explicitly drop that partition. Instead, you must either add additional partitions prior to dropping the partition or you may choose to drop the model itself. When dropping a partitioned model, all partitions are dropped in a single atomic operation. From a performance perspective, Oracle recommends DROP_PARTITION followed by an ADD_PARTITION instead of leveraging the REPLACE option due to the efficient behavior of the DROP_PARTITION option.
Oracle Data Mining supports adding a single partition or multiple partitions to an existing partitioned model.
The addition occurs based on the input data set and the name of the existing partitioned model. The operation takes the input data set and the existing partitioned model as parameters. The partition keys are extracted from the input data set and the model partitions are built against the input data set. These partitions are added to the partitioned model. In the case where partition keys for new partitions conflict with the existing partitions in the model, you can select from the following three approaches to resolve the conflicts:
ERROR: Terminates the ADD operation without adding any partitions.
REPLACE: Replaces the existing partition for which the conflicting keys are found.
IGNORE: Eliminates the rows having the conflicting keys.
If the input data set contains multiple keys, then the operation creates multiple partitions. If the total number of partitions in the model increases to more than the user-defined maximum specified when the model was created, then you get an error. The default threshold value for the number of partitions is 1000.
Learn about scoring of a partitioned model.
The scoring of the partitioned model is the same as that of the non-partitioned model. The syntax of the data mining function remains the same but is extended to provide an optional hint to you. The optional hint can impact the performance of a query which involves scoring a partitioned model.
For scoring a partitioned model, the signature columns used during the build for the partitioning key must be present in the scoring data set. These columns are combined to form a unique partition key. The unique key is then mapped to a specific underlying model partition, and the identified model partition is used to score that row.
The partitioned objects that are necessary for scoring are loaded on demand during the query execution and are aged out depending on the System Global Area (SGA) memory.
Related Topics
Understand how to configure data mining models at build time.
Numerous configuration settings are available for configuring data mining models at build time. To specify settings, create a settings table with the columns shown in the following table and pass the table to CREATE_MODEL.
Table 25-4 Settings Table Required Columns
| Column Name | Data Type |
|---|---|
|
|
|
|
|
|
Example 25-1 creates a settings table for an Support Vector Machine (SVM) Classification model. Since SVM is not the default classifier, the ALGO_NAME setting is used to specify the algorithm. Setting the SVMS_KERNEL_FUNCTION to SVMS_LINEAR causes the model to be built with a linear kernel. If you do not specify the kernel function, the algorithm chooses the kernel based on the number of attributes in the data.
Some settings apply generally to the model, others are specific to an algorithm. Model settings are referenced in Table 25-5 and Table 25-6.
Table 25-5 General Model Settings
| Settings | Description |
|---|---|
|
Mining function settings |
See "Mining Function Settings" in Oracle Database PL/SQL Packages and Types Reference |
|
Algorithm names |
See "Algorithm Names" in Oracle Database PL/SQL Packages and Types Reference |
|
Global model characteristics |
See "Global Settings" in Oracle Database PL/SQL Packages and Types Reference |
|
Automatic Data Preparation |
See "Automatic Data Preparation" in Oracle Database PL/SQL Packages and Types Reference |
Table 25-6 Algorithm-Specific Model Settings
| Algorithm | Description |
|---|---|
|
Decision Tree |
See "Algorithm Settings: Decision Tree" in Oracle Database PL/SQL Packages and Types Reference |
|
Expectation Maximization |
See "Algorithm Settings: Expectation Maximization" in Oracle Database PL/SQL Packages and Types Reference |
|
Explicit Semantic Analysis |
See “Algorithm Settings: Explicit Semantic Analysis” in Oracle Database PL/SQL Packages and Types Reference |
|
Generalized Linear Models |
See "Algorithm Settings: Generalized Linear Models" in Oracle Database PL/SQL Packages and Types Reference |
|
k-Means |
See "Algorithm Settings: k-Means" in Oracle Database PL/SQL Packages and Types Reference |
|
Naive Bayes |
See "Algorithm Settings: Naive Bayes" in Oracle Database PL/SQL Packages and Types Reference |
|
Non-Negative Matrix Factorization |
See "Algorithm Settings: Non-Negative Matrix Factorization" in Oracle Database PL/SQL Packages and Types Reference |
|
O-Cluster |
See "Algorithm Settings: O-Cluster" in Oracle Database PL/SQL Packages and Types Reference |
|
Singular Value Decomposition |
See "Algorithm Settings: Singular Value Decomposition" in Oracle Database PL/SQL Packages and Types Reference |
|
Support Vector Machine |
See "Algorithm Settings: Support Vector Machine" in Oracle Database PL/SQL Packages and Types Reference |
Example 25-1 Creating a Settings Table for an SVM Classification Model
CREATE TABLE svmc_sh_sample_settings (
setting_name VARCHAR2(30),
setting_value VARCHAR2(4000));
BEGIN
INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES
(dbms_data_mining.algo_name, dbms_data_mining.algo_support_vector_machines);
INSERT INTO svmc_sh_sample_settings (setting_name, setting_value) VALUES
(dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_linear);
COMMIT;
END;
/
Related Topics
Specify a cost matrix table to build a Decision Tree model.
The CLAS_COST_TABLE_NAME setting specifies the name of a cost matrix table to be used in building a Decision Tree model. A cost matrix biases a classification model to minimize costly misclassifications. The cost matrix table must have the columns shown in the following table:
Table 25-7 Cost Matrix Table Required Columns
| Column Name | Data Type |
|---|---|
|
|
valid target data type |
|
|
valid target data type |
|
|
|
Decision Tree is the only algorithm that supports a cost matrix at build time. However, you can create a cost matrix and associate it with any classification model for scoring.
If you want to use costs for scoring, create a table with the columns shown in Table 25-7, and use the DBMS_DATA_MINING.ADD_COST_MATRIX procedure to add the cost matrix table to the model. You can also specify a cost matrix inline when invoking a PREDICTION function. Table 23-1 has details for valid target data types.
Related Topics
Prior probabilities can be used to offset differences in distribution between the build data and the actual population.
The CLAS_PRIORS_TABLE_NAME setting specifies the name of a table of prior probabilities to be used in building a Naive Bayes model. The priors table must have the columns shown in the following table.
Table 25-8 Priors Table Required Columns
| Column Name | Data Type |
|---|---|
|
|
valid target data type |
|
|
|
Related Topics
Specify class weights table settings in Logistic Regression or Support Vector Machine (SVM) Classification to favour higher weighted classes.
The CLAS_WEIGHTS_TABLE_NAME setting specifies the name of a table of class weights to be used to bias a logistic regression (Generalized Linear Model Classification) or SVM Classification model to favor higher weighted classes. The weights table must have the columns shown in the following table.
Table 25-9 Class Weights Table Required Columns
| Column Name | Data Type |
|---|---|
|
|
valid target data type |
|
|
|
Related Topics
Explains about ALL/USER/DBA_MINING_MODEL_SETTINGS in data dictionary view.
Information about mining model settings can be obtained from the data dictionary view ALL/USER/DBA_MINING_MODEL_SETTINGS. When used with the ALL prefix, this view returns information about the settings for the models accessible to the current user. When used with the USER prefix, it returns information about the settings for the models in the user's schema. The DBA prefix is only available for DBAs.
The columns of ALL_MINING_MODEL_SETTINGS are described as follows and explained in the following table.
SQL> describe all_mining_model_settings Name Null? Type ----------------------------------------- -------- ---------------------------- OWNER NOT NULL VARCHAR2(30) MODEL_NAME NOT NULL VARCHAR2(30) SETTING_NAME NOT NULL VARCHAR2(30) SETTING_VALUE VARCHAR2(4000) SETTING_TYPE VARCHAR2(7)
Table 25-10 ALL_MINING_MODEL_SETTINGS
| Column | Description |
|---|---|
|
|
Owner of the mining model. |
|
|
Name of the mining model. |
|
|
Name of the setting. |
|
|
Value of the setting. |
|
|
|
The following query lists the settings for the Support Vector Machine (SVM) Classification model SVMC_SH_CLAS_SAMPLE. The ALGO_NAME, CLAS_WEIGHTS_TABLE_NAME, and SVMS_KERNEL_FUNCTION settings are user-specified. These settings have been specified in a settings table for the model.
Example 25-2 ALL_MINING_MODEL_SETTINGS
SQL> COLUMN setting_value FORMAT A35
SQL> SELECT setting_name, setting_value, setting_type
FROM all_mining_model_settings
WHERE model_name in 'SVMC_SH_CLAS_SAMPLE';
SETTING_NAME SETTING_VALUE SETTING
------------------------------ ----------------------------------- -------
SVMS_ACTIVE_LEARNING SVMS_AL_ENABLE DEFAULT
PREP_AUTO OFF DEFAULT
SVMS_COMPLEXITY_FACTOR 0.244212 DEFAULT
SVMS_KERNEL_FUNCTION SVMS_LINEAR INPUT
CLAS_WEIGHTS_TABLE_NAME svmc_sh_sample_class_wt INPUT
SVMS_CONV_TOLERANCE .001 DEFAULT
ALGO_NAME ALGO_SUPPORT_VECTOR_MACHINES INPUT
Related Topics
The mining model settings for R model determine the characteristics of the model. You can specify the mining model settings in the mining_model_table.
ODMS_PARTITION_COLUMNS and ODMS_SAMPLING. The following settings are exclusive to R mining model, and they allow you to specify the R Mining model:
Related Topics
Use the ALGO_EXTENSIBLE_LANG setting to specify the Oracle Data Mining framework with extensible algorithms.
Currently, R is the only valid value for ALGO_EXTENSIBLE_LANG. When the value for ALGO_EXTENSIBLE_LANG is set to R, the mining models are built using the R language. You can use the following settings in the model_setting_table to specify the build, score, and view of the R model.
Related Topics
Use the RALG_BUILD_FUNCTION to specify the name of an existing registered R script for R algorithm mining model build.
RALG_BUILD_FUNCTION and ALGO_EXTENSIBLE_LANG in the model_setting_table. The R script defines an R function that has the first input argument of data.frame for training data, and it returns an R model object. The first data argument is mandatory. The RALG_BUILD_FUNCTION can accept additional model build parameters.
Note:
The valid inputs for input parameters are numeric and string scalar data types.Example 25-3 Example of RALG_BUILD_FUNCTION
This example shows how to specify the name of the R script MY_LM_BUILD_SCRIPT that is used to build the model in the model_setting_table.
Begin insert into model_setting_table values (dbms_data_mining.ralg_build_function,'MY_LM_BUILD_SCRIPT'); End; /
The R script MY_LM_BUILD_SCRIPT defines an R function that builds the LM model. You must register the script MY_LM_BUILD_SCRIPT in the R script repository which uses the existing ORE security restrictions. You can use Oracle R Enterprise API sys.rqScriptCreate to register the script. Oracle R Enterprise requires the RQADMIN role to register R scripts.
For example:
Begin
sys.rqScriptCreate('MY_LM_BUILD_SCRIPT', 'function(data, formula, model.frame) {lm(formula = formula, data=data, model = as.logical(model.frame)}');
End;
/
For Clustering and Feature Extraction mining function model build, the R attributes dm$nclus and dm$nfeat must be set on the return R model to indicate the number of clusters and features respectively.
The R script MY_KM_BUILD_SCRIPT defines an R function that builds the k-Means model for Clustering. R attribute dm$nclus is set with the number of clusters for the return Clustering model.
'function(dat) {dat.scaled <- scale(dat)
set.seed(6543); mod <- list()
fit <- kmeans(dat.scaled, centers = 3L)
mod[[1L]] <- fit
mod[[2L]] <- attr(dat.scaled, "scaled:center")
mod[[3L]] <- attr(dat.scaled, "scaled:scale")
attr(mod, "dm$nclus") <- nrow(fit$centers)
mod}'
The R script MY_PCA_BUILD_SCRIPT defines an R function that builds the PCA model. R attribute dm$nfeat is set with the number of features for the return feature extraction model.
'function(dat) {
mod <- prcomp(dat, retx = FALSE)
attr(mod, "dm$nfeat") <- ncol(mod$rotation)
mod}'
Related Topics
The RALG_BUILD_FUNCTION input parameter specifies a list of numeric and string scalar values in SQL SELECT query statement format.
Example 25-4 Example of RALG_BUILD_PARAMETER
The RALG_BUILD_FUNCTION input parameters must be a list of numeric and string scalar values. The input parameters are optional.
'SELECT value parameter name ...FROM dual'
'formula' and a numeric value zero for input argument 'model.frame' using the RALG_BUILD_PARAMETER. These input arguments must match with the function signature of the R script used in RALG_BUILD_FUNCTION Parameter.
Begin insert into model_setting_table values (dbms_data_mining.ralg_build_parameter, 'select ''AGE ~ .'' as "formula", 0 as "model.frame" from dual'); End; /
Related Topics
The RALG_DETAILS_FUNCTION specifies the R model metadata that is returned in the data.frame.
Use the RALG_DETAILS_FUNCTION to specify an existing registered R script that generates model information. The specified R script defines an R function that contains the first input argument for the R model object. The output of the R function must be a data.frame. The columns of the data.frame are defined by RALG_DETAILS_FORMAT, and can contain only numeric or string scalar types.
Example 25-5 Example of RALG_DETAILS_FUNCTION
MY_LM_DETAILS_SCRIPT in the model_setting_table. This script defines the R function that is used to provide the model information.
Begin insert into model_setting_table values (dbms_data_mining.ralg_details_function, 'MY_LM_DETAILS_SCRIPT'); End; /
MY_LM_DETAILS_SCRIPT is registered as:
'function(mod) data.frame(name=names(mod$coefficients),
coef=mod$coefficients)'
Related Topics
Use the RALG_DETAILS_FORMAT parameter to specify the names and column types in the model view. It is a string that contains a SELECT query to specify a list of numeric and string scalar data types for the name and type of the model view columns.
When RALG_DETAILS_FORMAT and RALG_DETAILS_FUNCTION are both specified, a model view by the name DM$VD <model_name> is created along with an R model in the current schema. The first column of the model view is PARTITION_NAME. It has NULL value for non-partitioned models. The other columns of the model view are defined by RALG_DETATLS_FORMAT.
Example 25-6 Example of RALG_DETAILS_FORMAT
varchar2 column attr_name and number column coef_value after the first column partition_name.
Begin
insert into model_setting_table values
(dbms_data_mining.ralg_details_format, 'select cast(''a'' as varchar2(20)) as attr_name, 0 as coef_value from dual');
End;
/
Related Topics
Use the RALG_SCORE_FUNCTION to specify an existing registered R script for R algorithm mining model score in the mining_model_table.
The specified R script defines an R function. The first input argument defines the model object. The second input argument defines the data.frame that is used for scoring data.
Example 25-7 Example of RALG_SCORE_FUNCTION
data.frame. The argument object is the R Linear Model. The argument newdata contains scoring data in the data.frame.
function(object, newdata) {res <- predict.lm(object, newdata = newdata, se.fit = TRUE); data.frame(fit=res$fit, se=res$se.fit, df=summary(object)$df[1L])}
In this example,
object indicates the LM model
newdata indicates the scoring data.frame
The output of the specified R function must be a data.frame. Each row represents the prediction for the corresponding scoring data from the input data.frame. The columns of the data.frame are specific to mining functions, such as:
Regression: A single numeric column for predicted target value, with two optional columns containing standard error of model fit, and the degrees of freedom number. The optional columns are needed for query function PREDICTION_BOUNDS to work.
Example 25-8 Example of RALG_SCORE_FUNCTION for Regression
This example shows how to specify the name of the R script MY_LM_PREDICT_SCRIPT that is used to score the model in the model_setting_table.
Begin insert into model_setting_table values (dbms_data_mining.ralg_score_function, 'MY_LM_PREDICT_SCRIPT'); End; /
MY_LM_PREDICT_SCRIPT is registered as:
function(object, newdata) {data.frame(pre = predict(object, newdata = newdata))}
Classification: Each column represents the predicted probability of one target class. The column name is the target class name.
Example 25-9 Example of RALG_SCORE_FUNCTION for Classification
This example shows how to specify the name of the R script MY_LOGITGLM_PREDICT_SCRIPT that is used to score the logit Classification model in the model_setting_table.
Begin insert into model_setting_table values (dbms_data_mining.ralg_score_function, 'MY_LOGITGLM_PREDICT_SCRIPT'); End; /
MY_LOGITGLM_PREDICT_SCRIPT is registered as follows. It is a logit Classification with two target class "0" and "1".
'function(object, newdata) {
pred <- predict(object, newdata = newdata, type="response");
res <- data.frame(1-pred, pred);
names(res) <- c("0", "1");
res}'
Clustering: Each column represents the predicted probability of one cluster. The columns are arranged in order of cluster ID. Each cluster is assigned a cluster ID, and they are consecutive values starting from 1. To support CLUSTER_DISTANCE in the R model, the output of R score function returns extra column containing the value of the distance to each cluster in order of cluster ID after the columns for the predicted probability.
Example 25-10 Example of RALG_SCORE_FUNCTION for Clustering
This example shows how to specify the name of the R script MY_CLUSTER_PREDICT_SCRIPT that is used to score the model in the model_setting_table.
Begin insert into model_setting_table values (dbms_data_mining.ralg_score_function, 'MY_CLUSTER_PREDICT_SCRIPT'); End; /
MY_CLUSTER_PREDICT_SCRIPT is registered as:
'function(object, dat){
mod <- object[[1L]]; ce <- object[[2L]]; sc <- object[[3L]];
newdata = scale(dat, center = ce, scale = sc);
centers <- mod$centers;
ss <- sapply(as.data.frame(t(centers)),
function(v) rowSums(scale(newdata, center=v, scale=FALSE)^2));
if (!is.matrix(ss)) ss <- matrix(ss, ncol=length(ss));
disp <- -1 / (2* mod$tot.withinss/length(mod$cluster));
distr <- exp(disp*ss);
prob <- distr / rowSums(distr);
as.data.frame(cbind(prob, sqrt(ss)))}'
Feature Extraction: Each column represents the coefficient value of one feature. The columns are arranged in order of feature ID. Each feature is assigned a feature ID, and they are consecutive values starting from 1.
Example 25-11 Example of RALG_SCORE_FUNCTION for Feature Extraction
This example shows how to specify the name of the R script MY_FEATURE_EXTRACTION_SCRIPT that is used to score the model in the model_setting_table.
Begin insert into model_setting_table values (dbms_data_mining.ralg_score_function, 'MY_FEATURE_EXTRACTION_SCRIPT'); End; /
MY_FEATURE_EXTRACTION_SCRIPT is registered as:
'function(object, dat) { as.data.frame(predict(object, dat)) }'
The function fetches the centers of the features from the R model, and computes the feature coefficient based on the distance of the score data to the corresponding feature center.
Related Topics
Use the RALG_WEIGHT_FUNCTION to specify the name of an existing registered R script that computes weight or contribution for each attribute in scoring. The specified R script is used in the query function PREDICTION_DETAILS to evaluate attribute contribution.
The specified R script defines an R function containing the first input argument for model object, and the second input argument of data.frame for scoring data. When the mining function is Classification, Clustering, or Feature Extraction, the target class name or cluster ID or feature ID is passed by the third input argument to compute the weight for that particular class or cluster or feature. The script returns a data.frame containing the contributing weight for each attribute in a row. Each row corresponds to that input scoring data.frame.
Example 25-12 Example of RALG_WEIGHT_FUNCTION
MY_PREDICT_WEIGHT_SCRIPT that computes weight or contribution of R model attributes in the model_setting_table.
Begin insert into model_setting_table values (dbms_data_mining.ralg_weight_function, 'MY_PREDICT_WEIGHT_SCRIPT'); End; /
MY_PREDICT_WEIGHT_SCRIPT for Regression is registered as:
'function(mod, data) { coef(mod)[-1L]*data }'
MY_PREDICT_WEIGHT_SCRIPT for logit Classification is registered as:
'function(mod, dat, clas) {
v <- predict(mod, newdata=dat, type = "response");
v0 <- data.frame(v, 1-v); names(v0) <- c("0", "1");
res <- data.frame(lapply(seq_along(dat),
function(x, dat) {
if(is.numeric(dat[[x]])) dat[,x] <- as.numeric(0)
else dat[,x] <- as.factor(NA);
vv <- predict(mod, newdata = dat, type = "response");
vv = data.frame(vv, 1-vv); names(vv) <- c("0", "1");
v0[[clas]] / vv[[clas]]}, dat = dat));
names(res) <- names(dat);
res}'
Related Topics
The RALG_*_FUNCTION must specify R scripts that exist in the R script repository. You can register the R scripts using Oracle R Enterprise.
The RALG_*_FUNCTION includes the following functions:
RALG_BUILD_FUNCTION
RALG_DETAILS_FUNCTION
RALG_SCORE_FUNCTION
RALG_WEIGHT_FUNCTION
Note:
The R scripts must exist in the R script repository for an R model to function.You can register the R scripts through Oracle Enterprise R (ORE). To register R scripts, you must have the RQADMIN role. After an R model is built, the names of these specified R scripts become model settings. These R scripts must exist in the R script repository for an R model to remain functional.
You can manage the R memory that is used to build, score, and view the R models through Oracle Enterprise R as well.
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:
Model detail views for Association Rules describes the rule view for Association Rules. Oracle recommends that users leverage the model details views instead of the GET_ASSOCIATION_RULES function.
The rule view DM$VRmodel_name describes the generated rules for Association Rules. Depending on the settings of the model, the rule has different set of columns. The following views are displayed when different Global settings are applied without aggregates for transactional and 2–Dimensional inputs.
Transactional Input Without ASSO_AGGREGATES Setting
ODMS_ITEM_ID_COLUMN_NAME is set and ITEM_VALUE (ODMS_ITEM_VALUE_COLUMN_NAME) is not set, the following is the transactional view:
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 25-11 Rule View Columns for Transactional Inputs
| Column Name | Description |
|---|---|
|
|
A partition in a partitioned model to retrieve details |
|
|
Name or identifier of the target |
|
|
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. |
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The ratio of the number of transactions that satisfy the antecedent to the total number of transactions. |
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The total number of attributes referenced in the antecedent and consequent of the rule. |
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The ratio of the number of transactions that satisfy the consequent to the total number of transactions. |
|
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Name of the consequent |
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For two-dimensional inputs, For two-dimensional inputs, when 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. This column is not applicable when ASSO_AGGREGATES is set. |
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Value of the consequent when In the following view, the 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_VALUE NUMBER ANTECEDENT SYS.XMLTYPE When In the following view, the 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_VALUE VARCHAR2(4000) ANTECEDENT SYS.XMLTYPE |
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The independent condition in the rule. When this condition exists, the dependent condition in the consequent also exists. The condition is a combination of attribute values called a predicate (
Note: The occurrence of the attribute as aDM_PREDICATE indicates the presence of the item in the transaction. The actual value for attribute_num_value or attribute_str_value is meaningless. For example, the following predicate indicates that 'Mouse Pad' is present in the transaction even though the attribute value is NULL.
|
Transactional Input With ASSO_AGGREGATES Setting
Rule view when ODMS_ITEM_ID_COLUMN_NAME is set and Item_value (ODMS_ITEM_VALUE_COLUMN_NAME) is not set.
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.
ASSO_AGGREGATES has columns for the aggregates output (four columns per aggregate). The 2–Dimensional input does not allow aggregates setting.Example 25-13 Examples
The following example shows profit and sales set to be aggregated:
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
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.
Name Null? 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_VALUE NUMBER 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 categorical, the view has a CONSEQUENT_VALUE column.
Name Null? 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_VALUE 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
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$VImodel_name has the following schema:
Name Type ------------- ------------------ PARTITION_NAME VARCHAR2 (128) ITEMSET_ID NUMBER SUPPORT NUMBER NUMBER_OF_ITEMS NUMBER ITEMSET SYS.XMLTYPE
Table 25-12 Frequent Itemsets View
| Column Name | Description |
|---|---|
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A partition in a partitioned model |
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Itemset identifier |
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Support of the itemset |
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Number of items in the itemset |
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|
Frequent itemset The structure of the |
Model detail view for Transactional Itemsets describes the transactional itemsets view. Oracle recommends that users leverage the model details views.
For the very common case of transactional data without aggregates, DM$VTmodel_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 25-13 Transactional Itemsets View
| Column Name | Description |
|---|---|
|
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A partition in a partitioned model |
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Itemset identifier |
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Item identifier |
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Support of the itemset |
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Number of items in the itemset |
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The name of the item |
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$VAmodel_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 25-14 Transactional Rule View
| Column Name | Description |
|---|---|
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A partition in a partitioned model |
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Rule identifier |
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Name of the Antecedent item. |
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Name of the Consequent item |
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Support of the rule |
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The likelihood a transaction satisfies the rule when it contains the Antecedent. |
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The degree of improvement in the prediction over random chance when the rule is satisfied |
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|
The number of transactions in which the rule occurs divided by the number of transactions in which the consequent occurs |
|
|
Itemset identifier |
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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 |
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Number of items in the rule |
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$VTmodel_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 25-15 Target Map View
| Column Name | Description |
|---|---|
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Partition name in a partitioned model |
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Target value, numerical or categorical |
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Number of rows for a given |
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Weight for a given |
The scoring cost view DM$VCmodel_name describes the scoring cost matrix for Classification models. The view has the following schema:
Name Type ----------------------------------------- -------------------------------- PARTITION_NAME VARCHAR2(128) ACTUAL_TARGET_VALUE NUMBER/VARCHAR2 PREDICTED_TARGET_VALUE NUMBER/VARCHAR2 COST NUMBER
Table 25-16 Scoring Cost View
| Column Name | Description |
|---|---|
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Partition name in a partitioned model |
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A valid target value |
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Predicted target value |
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Associated cost for the actual and predicted target value pair |
Model detail view for Decision Tree describes the split information view, node statistics view, node description view, and the cost matrix view. Oracle recommends that users leverage the model details views instead of GET_MODEL_DETAILS_XML function.
The split information view DM$VPmodel_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 25-17 Split Information View
| Column Name | Description |
|---|---|
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Partition name in a partitioned model |
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Node ID of the parent |
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|
The main or surrogate split |
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|
The node ID |
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|
The attribute used as the splitting criterion at the parent node to produce this node. |
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|
Split attribute subname. The value is null for non-nested columns. |
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Split operator |
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Value used as the splitting criterion. This is an XML element described using the For example, |
The node statistics view DM$VImodel_name describes the statistics associated with individual tree nodes. The statistics include a target histogram for the data in the node. The view has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) NODE NUMBER NODE_SUPPORT NUMBER PREDICTED_TARGET_VALUE NUMBER/VARCHAR2 TARGET_VALUE NUMBER/VARCHAR2 TARGET_SUPPORT NUMBER
Table 25-18 Node Statistics View
| Parameter | Description |
|---|---|
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Partition name in a partitioned model |
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The node ID |
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Number of records in the training set that belong to the node |
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|
Predicted Target value |
|
|
A target value seen in the training data |
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|
The number of records that belong to the node and have the value specified in the |
Higher level node description can be found in DM$VOmodel_name view. The DM$VOmodel_name has the following schema:
ame Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) NODE NUMBER NODE_SUPPORT NUMBER PREDICTED_TARGET_VALUE NUMBER/VARCHAR2 PARENT NUMBER ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) OPERATOR VARCHAR2 VALUE SYS.XMLTYPE
Table 25-19 Node Description View
| Parameter | Description |
|---|---|
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Partition name in a partitioned model |
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The node ID |
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Number of records in the training set that belong to the node |
|
|
Predicted Target value |
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|
The ID of the parent |
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Specifies the attribute name |
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|
Specifies the attribute subname |
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Attribute predicate operator - a conditional operator taking the following values: IN, = , <>, < , >, <=, and >= |
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Value used as the description criterion. This is an XML element described using the For example, |
The DM$VMmodel_name view describes the cost matrix used by the Decision Tree build. The DM$VMmodel_name view has the following schema:
Name Type ----------------------------------------- -------------------------------- PARTITION_NAME VARCHAR2(128) ACTUAL_TARGET_VALUE NUMBER/VARCHAR2 PREDICTED_TARGET_VALUE NUMBER/VARCHAR2 COST NUMBER
Table 25-20 Cost Matrix View
| Parameter | Description |
|---|---|
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Partition name in a partitioned model |
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Valid target value |
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Predicted Target value |
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Associated cost for the actual and predicted target value pair |
Model details views for Generalized Linear Model (GLM) describes the model details view and row diagnostic view for Linear and Logistic Regression. Oracle recommends that users leverage model details views than the GET_MODEL_DETAILS_GLM function.
The model details view DM$VDmodel_name describes the final model information for both Linear Regression models and Logistic Regression models.
For Linear Regression, the view DM$VDmodel_name has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) ATTRIBUTE_VALUE VARCHAR2(4000) FEATURE_EXPRESSION VARCHAR2(4000) COEFFICIENT BINARY_DOUBLE STD_ERROR BINARY_DOUBLE TEST_STATISTIC BINARY_DOUBLE P_VALUE BINARY_DOUBLE VIF BINARY_DOUBLE STD_COEFFICIENT BINARY_DOUBLE LOWER_COEFF_LIMIT BINARY_DOUBLE UPPER_COEFF_LIMIT BINARY_DOUBLE
For Logistic Regression, the view DM$VDmodel_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 25-21 Model View for Linear and Logistic Regression Models
| Column Name | Description |
|---|---|
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The name of a feature in the model |
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Valid target value |
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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 non-nested columns. When the nested column is numeric, the mining attribute is identified by the combination |
|
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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 fully-qualified 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: fully-qualified_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 t-value of the coefficient estimate. For Logistic Regression, the Wald chi-square value of the coefficient estimate. |
|
|
Probability of the |
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|
Variance Inflation Factor. The value is zero for the intercept. For Logistic Regression, |
|
|
Standardized estimate of the coefficient. |
|
|
Lower confidence bound of the coefficient. |
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|
Upper confidence bound of the coefficient. |
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|
Exponentiated coefficient for Logistic Regression. For linear regression, |
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Exponentiated coefficient for lower confidence bound of the coefficient for Logistic Regression. For Linear Regression, |
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Exponentiated coefficient for upper confidence bound of the coefficient for Logistic Regression. For Linear Regression, |
The row diagnostic view DM$VAmodel_name describes row level information for both Linear Regression models and Logistic Regression models. For Linear Regression, the view DM$VAmodel_name has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) CASE_ID NUMBER/VARHCAR2 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 25-22 Row Diagnostic View for Linear Regression
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
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Name of the case identifier |
|
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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 ith row on the ith fitted value. Large Hat values are indicators that the ith row is a point of high leverage, a potential outlier. |
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|
The difference between the predicted and actual target value for a specific input row. |
|
|
The standard error residual, sometimes called the Studentized residual, re-scales 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 ith case on all of the estimated regression coefficients. |
For Logistic Regression, the view DM$VAmodel_name has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) CASE_ID NUMBER/VARHCAR2 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 25-23 Row Diagnostic View for Logistic Regression
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
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Name of the case identifier |
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The actual target value as taken from the input row |
|
|
Model estimate of the probability of the predicted target value. |
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|
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. |
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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 ith row residual divided by the variance of the ith row prediction. The variance of the prediction is the predicted probability multiplied by 1 minus the predicted probability.
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The Pearson residual is a re-scaled 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 ith row.
|
|
|
The |
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|
Measures the overall change in the fitted logits due to the deletion of the ith observation for all points including the one deleted (the ith 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. |
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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 ith observation for all points excluding the one deleted (the ith 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. |
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|
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 |
|
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A statistic that measures the change in the Pearson chi-square statistic that occurs when an observation is deleted from the input. It is computed as |
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$VPmodel_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 25-24 Prior View for Naive Bayes
| Column Name | Description |
|---|---|
|
|
The name of a feature in the model |
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Name of the target column |
|
|
Target value, numerical or categorical |
|
|
Prior probability for a given |
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|
Number of rows for a given |
The Naïve Bayes result view DM$VVmodel_view describes the conditional probabilities of the Naïve Bayes model. The view has the following schema:
Name Type ----------------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) TARGET_NAME VARCHAR2(128) TARGET_VALUE NUMBER/VARCHAR2 ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) ATTRIBUTE_VALUE VARCHAR2(4000) CONDITIONAL_PROBABILITY BINARY_DOUBLE COUNT NUMBER
Table 25-25 Result View for Naive Bayes
| Column Name | Description |
|---|---|
|
|
The name of a feature in the model |
|
|
Name of the target column |
|
|
Target value, numerical or categorical |
|
|
Column name |
|
|
Nested column subname. The value is null for non-nested columns. |
|
|
Mining attribute value for the column |
|
|
Conditional probability of a mining attribute for a given target |
|
|
Number of rows for a given mining attribute and a given target |
Model Detail View for Support Vector Machine describes linear coefficient view. Oracle recommends that users leverage the model details views instead of the GET_MODEL_DETAILS_SVM function.
The linear coefficient view DM$VLmodel_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 25-26 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 non-nested columns. |
|
|
Value of a categorical attribute |
|
|
Projection coefficient value |
|
|
Coefficient transformed on the original scale |
Oracle Data Mining supports these clustering algorithms: Expectation Maximization, k-Means, and Orthogonal Partitioning Clustering (O-Cluster).
All clustering algorithms share the following views:
Cluster description DM$VDmodel_name
Attribute statistics DM$VAmodel_name
Histogram statistics DM$VHmodel_name
Rule statistics DM$VRmodel_name
The cluster description view DM$VDmodel_name describes cluster level information about a clustering model. The view has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) CLUSTER_ID NUMBER CLUSTER_NAME NUMBER/VARCHAR2 RECORD_COUNT NUMBER PARENT NUMBER TREE_LEVEL NUMBER LEFT_CHILD_ID NUMBER RIGHT_CHILD_ID NUMBER
Table 25-27 Cluster Description View for Clustering Algorithm
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
The ID of a cluster in the model |
|
|
Specifies the label of the cluster |
|
|
Specifies the number of records |
|
|
The ID of the parent |
|
|
Specifies the number of splits from the root |
|
|
The ID of the child cluster on the left side of the split |
|
|
The ID of the child cluster on the right side of the split |
The attribute view DM$VAmodel_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 25-28 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 |
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The mode is the most frequent value of a categorical attribute |
The histogram view DM$VHmodel_name describes histogram level information about a Clustering model. The bin information as well as bin counts can be obtained from this view. The view has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) CLUSTER_ID NUMBER CLUSTER_NAME NUMBER/VARCHAR2 ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) BIN_ID NUMBER LOWER_BIN_BOUNDARY BINARY_DOUBLE UPPER_BIN_BOUNDARY BINARY_DOUBLE ATTRIBUTE_VALUE VARCHAR2(4000) COUNT NUMBER
Table 25-29 Histogram View for Clustering Algorithm
| Column Name | Description |
|---|---|
|
|
A partition in a partitioned model |
|
|
The ID of a cluster in the model |
|
|
Specifies the label of the cluster |
|
|
Specifies the attribute name |
|
|
Specifies the attribute subname |
|
|
Bin ID |
|
|
Numeric lower bin boundary |
|
|
Numeric upper bin boundary |
|
|
Categorical attribute value |
|
|
Histogram count |
The rule view DM$VRmodel_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 25-30 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 |
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$VOmodel_name describes the EM components. The component view contains information about their prior probabilities and what cluster they map to. The view has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) COMPONENT_ID NUMBER CLUSTER_ID NUMBER PRIOR_PROBABILITY BINARY_DOUBLE
Table 25-31 Component View
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
Unique identifier of a component |
|
|
The ID of a cluster in the model |
|
|
Component prior probability |
The mean and variance component view DM$VMmodel_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$VFmodel_name provides information about the parameters of the multi-valued Bernoulli distributions used by the EM model. The view has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) COMPONENT_ID NUMBER ATTRIBUTE_NAME VARCHAR2(4000) ATTRIBUTE_VALUE VARCHAR2(4000) FREQUENCY BINARY_DOUBLE
Table 25-32 Frequency Component View
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
Unique identifier of a component |
|
|
Column name |
|
|
Categorical attribute value |
|
|
The frequency of the multivalued Bernoulli distribution for the attribute/value combination specified by |
For 2-Dimensional columns, EM provides an attribute ranking similar to that of Attribute Importance. This ranking is based on a rank-weighted average over Kullback–Leibler divergence computed for pairs of columns. This unsupervised Attribute Importance is shown in the DM$VImodel_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 25-33 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$VBmodel_name view. This metric evaluates how much the observed joint distribution of two attributes diverges from the expected distribution under the assumption of independence. That is, the higher the value, the more dependent the two attributes are. The dependency value is scaled based on the size of the grid used for each pairwise computation. That ensures that all values fall within the [0; 1] range and are comparable. The view has the following schema:
Name Type ----------------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) ATTRIBUTE_NAME_1 VARCHAR2(128) ATTRIBUTE_NAME_2 VARCHAR2(128) DEPENDENCY BINARY_DOUBLE
Table 25-34 Kullback-Leibler Divergence for Expectation Maximization
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
Name of an attribute 1 |
|
|
Name of an attribute 2 |
|
|
Scaled pairwise Kullback-Leibler divergence |
The projection table DM$VPmodel_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 25-35 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 non-nested columns. |
|
|
Categorical attribute value |
|
|
Projection coefficient. The representation is sparse; only the non-zero coefficients are returned. |
Related Topics
Model detail views for k-Means (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 k-Means against the Clustering views. For an overview of the different views, refer to "Model Detail Views for Clustering Algorithms". For k-Means, the cluster description view DM$VDmodel_name has an additional column:
Name Type ---------------------------------- ---------------------------- DISPERSION BINARY_DOUBLE
Table 25-36 Cluster Description for k-Means
| Column Name | Description |
|---|---|
|
|
A measure used to quantify whether a set of observed occurrences are dispersed compared to a standard statistical model. |
The scoring view DM$VCmodel_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 25-37 Scoring View for k-Means
| 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 non-nested columns. |
|
|
Categorical attribute value |
|
|
Specifies the centroid value |
Related Topics
Model Detail Views for O-Cluster 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 O-Cluster 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 k-Means (KM). The following are the statistics views:
Cluster description DM$VDmodel_name
Attribute statistics DM$VAmodel_name
Rule statistics DM$VRmodel_name
Histogram statistics DM$VHmodel_name
The Cluster description view DM$VDmodel_name describes the O-Cluster components. The cluster description view has additional fields that specify the split predicate. The view has the following schema:
Name Type ---------------------------------- ---------------------------- ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) OPERATOR VARCHAR2(2) VALUE SYS.XMLTYPE
Table 25-38 Description View
| Column Name | Description |
|---|---|
|
|
Column name |
|
|
Nested column subname. The value is null for non-nested columns. |
|
|
Split operator |
|
|
List of split values |
SYS.XMLTYPE is as follows:
<Element>splitval1</Element>
The OC algorithm uses a histogram view DM$VHmodel_name with a different schema than EM and k-Means (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 25-39 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 non-nested columns. |
|
|
Unique identifier |
|
|
Bin label |
|
|
Bin histogram count |
Related Topics
Model Detail Views for Explicit Semantic Analysis (ESA) describes attribute statistics view and feature view. Oracle recommends that users leverage the model details views.
ESA algorithm has the following descriptive statistics views:
Attribute statistics DM$VAmodel_name
Features DM$VFmodel_name
The view DM$VAmodel_name has the following schema:
PARTITION_NAME VARCHAR2(128) FEATURE_ID NUMBER/VARCHAR2 ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) ATTRIBUTE_VALUE VARCHAR2(4000) COEFFICIENT BINARY_DOUBLE
Table 25-40 Attribute View for Explicit Semantic Analysis
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
Unique identifier of a feature as it appears in the training data |
|
|
Column name |
|
|
Nested column subname. The value is null for non-nested columns. |
|
|
Categorical attribute value |
|
|
A measure of the weight of the attribute with respect to the feature |
The view DM$VFmodel_name has a unique row for every feature in one view. This feature is helpful if the model was pre-built and the source training data are not available. The view has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) FEATURE_ID NUMBER/VARCHAR2
Table 25-41 Feature View 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 |
Model detail views for Non-Negative 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$VEmodel_name
H inverse matrix DM$VImodel_name
The view DM$VEmodel_name describes the encoding (H) matrix of an NMF model. The FEATURE_NAME column type may be either NUMBER or VARCHAR2. The view has the following schema definition.
Name Type ------------------- -------------------------- PARTITION_NAME VARCHAR2(128) FEATURE_ID NUMBER FEATURE_NAME NUMBER/VARCHAR2 ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) ATTRIBUTE_VALUE VARCHAR2(4000) COEFFICIENT BINARY_DOUBLE
Table 25-42 Encoding H Matrix View for Non-Negative Matrix Factorization
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
The ID of a feature in the model |
|
|
The name of a feature in the model |
|
|
Column name |
|
|
Nested column subname. The value is null for non-nested columns. |
|
|
Specifies the value of attribute |
|
|
The attribute encoding that represents its contribution to the feature |
The view DM$VImodel_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 25-43 Inverse H Matrix View for Non-Negative 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 non-nested columns. |
|
|
Specifies the value of attribute |
|
|
The attribute encoding that represents its contribution to the feature |
Model detail views for Singular Value Decomposition (SVD) describes S Matrix view, right-singular vectors view, and left-singular vector view. Oracle recommends that users leverage the model details views instead of the GET_MODEL_DETAILS_SVD function.
The DM$VEmodel_name view leverages the fact that each singular value in the SVD model has a corresponding principal component in the associated Principal Components Analysis (PCA) model to relate a common set of information for both classes of models. For a SVD model, it describes the content of the S matrix. When PCA scoring is selected as a build setting, the variance and percentage cumulative variance for the corresponding principal components are shown as well. The view has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) FEATURE_ID NUMBER FEATURE_NAME NUMBER/VARCHAR2 VALUE BINARY_DOUBLE VARIANCE BINARY_DOUBLE PCT_CUM_VARIANCE BINARY_DOUBLE
Table 25-44 S Matrix View
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
The ID of a feature in the model |
|
|
The name of a feature in the model |
|
|
The matrix entry value |
|
|
The variance explained by a component. This column is only present for SVD models with setting This column is non-null only if the build data is centered, either manually or because of the following setting: |
|
|
The percent cumulative variance explained by the components thus far. The components are ranked by the explained variance in descending order. This column is only present for SVD models with setting This column is non-null only if the build data is centered, either manually or because of the following setting: |
The SVD DM$VVmodel_view describes the right-singular 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 25-45 Right-singular 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 non-nested columns. |
|
|
Categorical attribute value. For numerical attributes, |
|
|
The matrix entry value |
DM$VUmodel_name describes the left-singular vectors of a SVD model. For a PCA model, it describes the projection of the data in the principal components. This view does not exist unless the settings dbms_data_mining.svds_u_matrix_output is set to dbms_data_mining.svds_u_matrix_enable. The view has the following schema:
Name Type ---------------------------------- ---------------------------- PARTITION_NAME VARCHAR2(128) CASE_ID NUMBER/VARHCAR2 FEATURE_ID NUMBER FEATURE_NAME NUMBER/VARCHAR2 VALUE BINARY_DOUBLE
Table 25-46 Left-singular Vectors of Singular Value Decomposition or Projection Data in Principal Components
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
Unique identifier of the row in the build data described by the U matrix projection. |
|
|
The ID of a feature in the model |
|
|
The name of a feature in the model |
|
|
The matrix entry value |
Related Topics
Model detail view for Minimum Description Length (for calculating Attribute Importance) describes Attribute Importance view. Oracle recommends that users leverage the model details views instead of the GET_MODEL_DETAILS_AI function.
The Attribute Importance view DM$VAmodel_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 25-47 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 non-nested columns. |
|
|
Importance value |
|
|
Rank based on importance |
The binning view DM$VB describes the bin boundaries used in the automatic data preparation.
The view has the following schema:
Name Type -------------------- -------------------- PARTITION_NAME VARCHAR2(128) ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) BIN_ID NUMBER LOWER_BIN_BOUNDARY BINARY_DOUBLE UPPER_BIN_BOUNDARY BINARY_DOUBLE ATTRIBUTE_VALUE VARCHAR2(4000)
Table 25-48 Model Details View for Binning
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
Specifies the attribute name |
|
|
Specifies the attribute subname |
|
|
Bin ID (or bin identifier) |
|
|
Numeric lower bin boundary |
|
|
Numeric upper bin boundary |
|
|
Categorical value |
Model detail views for Global Information describes global statistics view, alert view, and computed settings view. Oracle recommends that users leverage the model details views instead of GET_MODEL_DETAILS_GLOBAL function.
The global statistics view DM$VGmodel_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 25-49 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$VWmodel_name lists alerts issued during the model build. The view has the following schema:
Name Type ------------------- ---------------------- PARTITION_NAME VARCHAR2(128) ERROR_NUMBER BINARY_DOUBLE ERROR_TEXT VARCHAR2(4000)
Table 25-50 Alert View
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
Error number (valid when event is Error) |
|
|
Error message |
The computed settings view DM$VSmodel_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 25-51 Computed Settings View
| Column Name | Description |
|---|---|
|
|
Partition name in a partitioned model |
|
|
Name of the setting |
|
|
Value of the setting |
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 25-52 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 non-nested columns. |
|
|
Numeric missing value replacement |
|
|
Categorical missing value replacement |
|
|
Normalization shift value |
|
|
Normalization scale value |