About the ore.predict Function

9.1 About the ore.predict Function

Predictive models allow you to predict future behavior based on past behavior.

After you build a model, you use it to score new data, that is, to make predictions.

R allows you to build many kinds of models. When you score data to predict new results using an R model, the data to score must be in an R data.frame. With the ore.predict function, you can use an R model to score database-resident data in an ore.frame object.

The ore.predict function provides the fastest way to operationalize R-based models for scoring in Oracle Database. The function has no dependencies on PMML or any other plug-ins.

Some advantages of using the ore.predict function to score data in the database are the following:

Uses R-generated models to score in-database data.

The data to score is in an ore.frame object.
Maximizes the use of Oracle Database as a compute engine.

The database provides a commercial grade, high performance, scalable scoring engine.
Simplifies application workflow.

You can go from a model to SQL scoring in one step.

The ore.predict function is a generic function. It has the following usage:

ore.predict(object, newdata, ...)

The value of the object argument is one of the model objects listed in Table 9-1. The value of the newdata argument is an ore.frame object that contains the data to score. The ore.predict function has methods for use with specific R model classes. The ... argument represents the various additional arguments that are accepted by the different methods.

Function ore.predict has methods that support the model objects listed in the table.

Table 9-1 Models Supported by the ore.predict Function

Class of Model	Description of Model
`glm`	Generalized Linear Model
`kmeans`	k-Means clustering model
`lm`	Linear regression model
`matrix`	A `matrix` with no more than 1000 rows, for use in an `hclust` hierarchical clustering model
`multinom`	Multinomial log-linear model
`nnet`	Neural Network model
`ore.model`	An OML4R model from the `OREModels` package
`prcomp`	Principal components analysis on a matrix
`princomp`	Principal components analysis on a numeric matrix
`rpart`	Recursive partitioning and regression tree model

For the function signatures of the ore.predict methods, call the help function on the following, as in help("ore.predict-kmeans"):

ore.predict-glm
ore.predict-kmeans
ore.predict-lm
ore.predict-matrix
ore.predict-multinom
ore.predict-nnet
ore.predict-ore.model
ore.predict-prcomp
ore.predict-princomp
ore.predict-rpart

Parent topic: Prediction With R Models