## Package org.apache.mahout.classifier.df.mapreduce.partial

Partial-data mapreduce implementation of Random Decision Forests

See:
Description

Class Summary
PartialBuilder: Builds a random forest using partial data.
Step1Mapper: First step of the Partial Data Builder.
TreeID: Indicates both the tree and the data partition used to grow the tree

## Package org.apache.mahout.classifier.df.mapreduce.partial Description

## Partial-data mapreduce implementation of Random Decision Forests

The builder splits the data, using a FileInputSplit, among the mappers. Building the forest and estimating the oob error takes two job steps.

In the first step, each mapper is responsible for growing a number of trees with its partition's, loading the data instances in its `map()` function, then building the trees in the `close()` method. It uses the reference implementation's code to build each tree and estimate the oob error.

The second step is needed when estimating the oob error. Each mapper loads all the trees that does not belong to its own partition (were not built using the partition's data) and uses them to classify the partition's data instances. The data instances are loaded in the `map()` method and the classification is performed in the `close()` method.

Copyright © 2008–2014 The Apache Software Foundation. All rights reserved.