superml: Build Machine Learning Models Like Using Python's Scikit-Learn Library in R

The idea is to provide a standard interface to users who use both R and Python for building machine learning models. This package provides a scikit-learn's fit, predict interface to train machine learning models in R.

Version: 0.5.2
Depends: R (≥ 3.5), R6 (≥ 2.2)
Imports: data.table (≥ 1.10), Rcpp (≥ 1.0), assertthat (≥ 0.2), parallel, doParallel (≥ 1.0), Metrics (≥ 0.1)
LinkingTo: Rcpp, BH
Suggests: knitr, rlang, testthat, rmarkdown, naivebayes (≥ 0.9), ClusterR (≥ 1.1), FNN (≥ 1.1), ranger (≥ 0.10), caret (≥ 6.0), xgboost (≥ 0.6), glmnet (≥ 2.0), e1071 (≥ 1.7)
Published: 2020-02-24
Author: Manish Saraswat [aut, cre]
Maintainer: Manish Saraswat <manish06saraswat at gmail.com>
BugReports: https://github.com/saraswatmks/superml/issues
License: GPL-3 | file LICENSE
URL: https://github.com/saraswatmks/superml
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: superml results

Downloads:

Reference manual: superml.pdf
Vignettes: Guide to CountVectorizer
Guide to TfidfVectorizer
Introduction to SuperML
Package source: superml_0.5.2.tar.gz
Windows binaries: r-prerelease: superml_0.5.2.zip, r-release: superml_0.5.2.zip, r-oldrel: superml_0.5.1.zip
macOS binaries: r-prerelease: superml_0.5.2.tgz, r-release: superml_0.5.2.tgz, r-oldrel: superml_0.5.2.tgz
Old sources: superml archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=superml to link to this page.