FeatureHashing: Creates a Model Matrix via Feature Hashing with a Formula Interface

Feature hashing, also called as the hashing trick, is a method to transform features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix. Without looking up the indices in an associative array, it applies a hash function to the features and uses their hash values as indices directly. The method of feature hashing in this package was proposed in Weinberger et al. (2009). The hashing algorithm is the murmurhash3 from the digest package. Please see the README in https://github.com/wush978/FeatureHashing for more information.

Depends: R (≥ 3.1), methods
Imports: Rcpp (≥ 0.11), Matrix, digest (≥ 0.6.8), magrittr (≥ 1.5)
LinkingTo: Rcpp, digest (≥ 0.6.8), BH
Suggests: RUnit, glmnet, knitr, xgboost, rmarkdown
Published: 2015-10-18
Author: Wush Wu [aut, cre], Michael Benesty [aut, ctb]
Maintainer: Wush Wu <wush978 at gmail.com>
BugReports: https://github.com/wush978/FeatureHashing/issues
License: GPL (≥ 3) | file LICENSE
URL: https://github.com/wush978/FeatureHashing
NeedsCompilation: yes
SystemRequirements: C++11
Materials: README ChangeLog
CRAN checks: FeatureHashing results


Reference manual: FeatureHashing.pdf
Vignettes: FeatureHashing
Sentiment Analysis via FeatureHashing
Package source: FeatureHashing_0.9.1.1.tar.gz
Windows binaries: r-devel: FeatureHashing_0.9.1.1.zip, r-release: FeatureHashing_0.9.1.1.zip, r-oldrel: FeatureHashing_0.9.1.1.zip
OS X El Capitan binaries: r-release: FeatureHashing_0.9.1.1.tgz
OS X Mavericks binaries: r-oldrel: FeatureHashing_0.9.1.1.tgz
Old sources: FeatureHashing archive

Reverse dependencies:

Reverse imports: rFTRLProximal


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