RODM_create_dt_model: Create a Decision Tree (DT) model

In RODM: R interface to Oracle Data Mining

Description

This function creates a Decision tree (DT).

Usage

RODM_create_dt_model(database, 
                     data_table_name, 
                     case_id_column_name = NULL, 
                     target_column_name,
                     model_name = "DT_MODEL", 
                     auto_data_prep = TRUE,
                     cost_matrix = NULL,
                     gini_impurity_metric = TRUE,
                     max_depth = NULL, 
                     minrec_split = NULL,
                     minpct_split = NULL,
                     minrec_node = NULL,
                     minpct_node = NULL,
                     retrieve_outputs_to_R = TRUE, 
                     leave_model_in_dbms = TRUE,
                     sql.log.file = NULL)

Arguments

database	Database ODBC channel identifier returned from a call to RODM_open_dbms_connection
data_table_name	Database table/view containing the training dataset.
case_id_column_name	Row unique case identifier in data_table_name.
target_column_name	Target column name in data_table_name.
model_name	ODM Model name.
auto_data_prep	Whether or not ODM should invoke automatic data preparation for the build.
cost_matrix	User-specified cost matrix for the target classes.
gini_impurity_metric	Tree impurity metric: "IMPURITY_GINI" (default) or "IMPURITY_ENTROPY"
max_depth	Specifies the maximum depth of the tree, from root to leaf inclusive. The default is 7.
minrec_split	Specifies the minimum number of cases required in a node in order for a further split to be possible. Default is 20.
minpct_split	Specifies the minimum number of cases required in a node in order for a further split to be possible. Expressed as a percentage of all the rows in the training data. The default is 1 (1 per cent).
minrec_node	Specifies the minimum number of cases required in a child node. Default is 10.
minpct_node	Specifies the minimum number of cases required in a child node, expressed as a percentage of the rows in the training data. The default is 0.05 (.05 per cent).
retrieve_outputs_to_R	Flag controlling if the output results are moved to the R environment.
leave_model_in_dbms	Flag controlling if the model is deleted or left in RDBMS.
sql.log.file	File where to append the log of all the SQL calls made by this function.

Details

The Decision Tree algorithm produces accurate and interpretable models with relatively little user intervention and can be used for both binary and multiclass classification problems. The algorithm is fast, both at build time and apply time. The build process for Decision Tree is parallelized. Decision tree scoring is especially fast. The tree structure, created in the model build, is used for a series of simple tests. Each test is based on a single predictor. It is a membership test: either IN or NOT IN a list of values (categorical predictor); or LESS THAN or EQUAL TO some value (numeric predictor). The algorithm supports two homogeneity metrics, gini and entropy, for calculating the splits.

For more details on the algotithm implementation, parameters settings and characteristics of the ODM function itself consult the following Oracle documents: ODM Concepts, ODM Developer's Guide, Oracle SQL Packages: Data Mining, and Oracle Database SQL Language Reference (Data Mining functions), listed in the references below.

Value

If retrieve_outputs_to_R is TRUE, returns a list with the following elements:

model.model_settings	Table of settings used to build the model.
model.model_attributes	Table of attributes used to build the model.
dt.distributions	Target class disctributions at each tree node.
dt.nodes	Node summary information.

Author(s)

Pablo Tamayo [e-mail]

Ari Mozes [e-mail]

References

Oracle Data Mining Concepts 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/toc.htm

Oracle Data Mining Application Developer's Guide 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28131/toc.htm

Oracle Data Mining Administrator's Guide 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28130/toc.htm

Oracle Database PL/SQL Packages and Types Reference 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/appdev.111/b28419/d_datmin.htm#ARPLS192

Oracle Database SQL Language Reference (Data Mining functions) 11g Release 1 (11.1) http://download.oracle.com/docs/cd/B28359_01/server.111/b28286/functions001.htm#SQLRF20030

See Also

RODM_apply_model, RODM_drop_model

Examples

## Not run: 
DB <- RODM_open_dbms_connection(dsn="orcl11g", uid= "rodm", pwd = "rodm")

# Predicting survival in the sinking of the Titanic based on pasenger's sex, age, class, etc.
data(titanic3, package="PASWR")                                             # Load survival data from Titanic
ds <- titanic3[,c("pclass", "survived", "sex", "age", "fare", "embarked")]  # Select subset of attributes
ds[,"survived"] <- ifelse(ds[,"survived"] == 1, "Yes", "No")                # Rename target values
n.rows <- length(ds[,1])                                                    # Number of rows
random_sample <- sample(1:n.rows, ceiling(n.rows/2))   # Split dataset randomly in train/test subsets
titanic_train <- ds[random_sample,]                         # Training set
titanic_test <-  ds[setdiff(1:n.rows, random_sample),]      # Test set
RODM_create_dbms_table(DB, "titanic_train")   # Push the training table to the database
RODM_create_dbms_table(DB, "titanic_test")    # Push the testing table to the database

dt <- RODM_create_dt_model(database = DB,    # Create ODM DT classification model
                             data_table_name = "titanic_train", 
                             target_column_name = "survived", 
                             model_name = "DT_MODEL")

dt2 <- RODM_apply_model(database = DB,    # Predict test data
                         data_table_name = "titanic_test", 
                         model_name = "DT_MODEL",
                         supplemental_cols = "survived")

print(dt2$model.apply.results[1:10,])                                  # Print example of prediction results
actual <- dt2$model.apply.results[, "SURVIVED"]                
predicted <- dt2$model.apply.results[, "PREDICTION"]                
probs <- as.real(as.character(dt2$model.apply.results[, "'Yes'"]))       
table(actual, predicted, dnn = c("Actual", "Predicted"))              # Confusion matrix
library(verification)
perf.auc <- roc.area(ifelse(actual == "Yes", 1, 0), probs)            # Compute ROC and plot
auc.roc <- signif(perf.auc$A, digits=3)
auc.roc.p <- signif(perf.auc$p.value, digits=3)
roc.plot(ifelse(actual == "Yes", 1, 0), probs, binormal=T, plot="both", xlab="False Positive Rate", 
         ylab="True Postive Rate", main= "Titanic survival ODM DT model ROC Curve")
text(0.7, 0.4, labels= paste("AUC ROC:", signif(perf.auc$A, digits=3)))
text(0.7, 0.3, labels= paste("p-value:", signif(perf.auc$p.value, digits=3)))

dt        # look at the model details

RODM_drop_model(DB, "DT_MODEL")             # Drop the model
RODM_drop_dbms_table(DB, "titanic_train")   # Drop the database table
RODM_drop_dbms_table(DB, "titanic_test")    # Drop the database table

RODM_close_dbms_connection(DB)

## End(Not run)

RODM documentation built on May 2, 2019, 7:03 a.m.