The OREmodels
package contains functions with which you can build advanced analytical data models using ore.frame
objects. The OREmodels
functions are the following:
Table 41 Functions in the OREmodels Package
Function  Description 


Fits and uses a generalized linear model on data in an 

Fits a linear regression model on data in an 

Fits a neural network model on data in an 
ore.randomForest 
Creates a random forest classification model in parallel on data in an ore.frame . 

Fits a stepwise linear regression model on data in an 
Note:
In R terminology, the phrase "fits a model" is often synonymous with "builds a model". In this document and in the online help for Oracle R Enterprise functions, the phrases are used interchangeably.
The ore.glm
, ore.lm
, and ore.stepwise
functions have the following advantages:
The algorithms provide accurate solutions using outofcore QR factorization. QR factorization decomposes a matrix into an orthogonal matrix and a triangular matrix.
QR is an algorithm of choice for difficult rankdeficient models.
You can process data that does not fit into memory, that is, outofcore data. QR factors a matrix into two matrices, one of which fits into memory while the other is stored on disk.
The ore.glm
, ore.lm
and ore.stepwise
functions can solve data sets with more than one billion rows.
The ore.stepwise
function allows fast implementations of forward, backward, and stepwise model selection techniques.
The ore.neural
function has the following advantages:
It is a highly scalable implementation of neural networks, able to build a model on even billion row data sets in a matter of minutes. The ore.neural
function can be run in two modes: inmemory for small to medium data sets and distributed (outofcore) for large inputs.
Users can specify the activation functions on neurons on a perlayer basis; ore.neural
supports many different activation functions.
Users can specify a neural network topology consisting of any number of hidden layers, including none.