getTree.rf.madlib {PivotalR} | R Documentation |
This function is a wrapper of MADlib's random forest model get_tree
function. The model built using madlib.randomForest
is passed
as input to this function.
getTree.rf.madlib(object, k=1, ...)
object |
A random forest model object built using |
k |
Id of the tree to be retrieved. Can range between 1 and maximum number of trees in the forest. default is 1. |
... |
Arguments to be passed to or from other methods. |
A data frame object similar to R's getTree result.
Author: Predictive Analytics Team at Pivotal Inc.
Maintainer: Frank McQuillan, Pivotal Inc. fmcquillan@pivotal.io
[1] Documentation of random forest in MADlib 1.7, http://doc.madlib.net/latest/
madlib.randomForest
function to train a random forest model.
print.rf.madlib
function to print summary of a model fitted
through madlib.randomForest
predict.rf.madlib
is a wrapper for MADlib's predict function for
random forests.
madlib.lm
, madlib.glm
,
madlib.summary
, madlib.arima
, madlib.elnet
,
madlib.rpart
are all MADlib wrapper functions.
## Not run: ## set up the database connection ## Assume that .port is port number and .dbname is the database name cid <- db.connect(port = .port, dbname = .dbname, verbose = FALSE) x <- as.db.data.frame(abalone, conn.id = cid, verbose = FALSE) lk(x, 10) ## decision tree using abalone data, using default values of minsplit, ## maxdepth etc. key(x) <- "id" fit <- madlib.randomForest(rings < 10 ~ length + diameter + height + whole + shell, data=x) fit getTree.rf.madlib(fit, k=2) db.disconnect(cid) ## End(Not run)