5 Predicting with R Models

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, make predictions.

R allows you to build many kinds of models. When you predict new results (score data) using an R model, the data must be in an R frame. The ore.predict package, included with Oracle R Enterprise, allows you to use an R model to score data that is in an ore.frame, that is, database resident- data.

ore.predict() allows you to make predictions only using ore.frame objects; you cannot rebuild the model.

If you need to build models with data in a database table, consider building an Oracle Data Mining model using the OREdm package, described in In-Database Predictive Models in Oracle R Enterprise.

For more information, see the R help associated with ore.predict().

ore.predict for R Models

ore.predict() allows you to score (predict using) these R models:

lm()Linear regression models
glm() Generalized linear models
hclust() Hierarchical clustering models
kmeans() (k-Means clustering)
negbin() (glm.nb) Negative binomial generalized binomial models
nnet::multinom Multinomial log-linear model
nnet::nnet neural network models
rpart::rpart Recursive partitioning and regression tree models

Examples

This code builds a linear regression model irisModel (built using lm) on the iris data and then scores IRIS (a table that could be created by pushing iris to the database):

R> irisModel <- lm(Sepal.Length ~ ., data = iris)
R> IRIS <- ore.push(iris)
R> IRISpred <- ore.predict(irisModel, IRIS, se.fit = TRUE, interval = "prediction")
R> IRIS <- cbind(IRIS, IRISpred)
R> head(IRIS)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species     PRED    SE.PRED LOWER.PRED UPPER.PRED
1          5.1         3.5          1.4         0.2  setosa 5.004788 0.04479188   4.391895   5.617681
2          4.9         3.0          1.4         0.2  setosa 4.756844 0.05514933   4.140660   5.373027
3          4.7         3.2          1.3         0.2  setosa 4.773097 0.04690495   4.159587   5.386607
4          4.6         3.1          1.5         0.2  setosa 4.889357 0.05135928   4.274454   5.504259
5          5.0         3.6          1.4         0.2  setosa 5.054377 0.04736842   4.440727   5.668026
6          5.4         3.9          1.7         0.4  setosa 5.388886 0.05592364   4.772430   6.005342