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) <arXiv:0902.2206>. The hashing algorithm is the murmurhash3 from the 'digest' package. Please see the README in <https://github.com/wush978/FeatureHashing> for more information.

Version: 0.9.1.3
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: 2018-04-10
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

Downloads:

Reference manual: FeatureHashing.pdf
Vignettes: FeatureHashing
Sentiment Analysis via FeatureHashing
Package source: FeatureHashing_0.9.1.3.tar.gz
Windows binaries: r-prerel: FeatureHashing_0.9.1.3.zip, r-release: FeatureHashing_0.9.1.3.zip, r-oldrel: FeatureHashing_0.9.1.3.zip
OS X binaries: r-prerel: FeatureHashing_0.9.1.1.tgz, r-release: FeatureHashing_0.9.1.1.tgz
Old sources: FeatureHashing archive

Reverse dependencies:

Reverse imports: rFTRLProximal

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