Provides R-implementation of Decision forest algorithm, which combines the predictions of
multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel
pattern-recognition method which can be used to analyze: (1) DNA microarray data;
(2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) data; and
(3) Structure-Activity Relation (SAR) data.
In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV.
run Dforest() to see more instructions.
Weida Tong (2003)
|Author||Leihong Wu <[email protected]>, Weida Tong ([email protected])|
|Date of publication||2017-11-28 22:03:57 UTC|
|Maintainer||Leihong Wu <[email protected]>|
|Package repository||View on CRAN|
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