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SoftRandomForest (version 0.1.0)

Classification Random Forests for Soft Decision Trees

Description

Performs random forests for soft decision trees for a classification problem. Current limitations are for a maximum depth of 5 resulting in 16 terminal nodes. Some data cleaning is required before input. Final graphic output requires currently requires exporting to 'Microsoft Excel' for visualization. Method based on Irsoy, Yildiz and Alpaydin (2012, ISBN: 978-4-9906441-1-6).

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install.packages('SoftRandomForest')

Monthly Downloads

6

Version

0.1.0

License

CC0

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Maintainer

Gregory Hilleren

Last Published

May 15th, 2019

Functions in SoftRandomForest (0.1.0)

BestForestSplit

Choosing the best variable for splitting.
SoftClassForest

Implementing a Random Forest of SDTs.
SoftForestPredDepth1

Building a single level for the Random Forest of SDTs.
SoftClassMatrix

Converting response vector to sparse matrix.
SoftForestPredFeeder

Choosing the appropriate depth function.
SoftObservation

Recording the prediction weights to analyze observation-level patterns
ClassMode

Determining the mode from a vector of numbers.