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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: | 0.5.7 |
| Depends: | R (≥ 3.6), R6 (≥ 2.2) |
| Imports: | data.table (≥ 1.10), Rcpp (≥ 1.0), assertthat (≥ 0.2), Metrics (≥ 0.1) |
| LinkingTo: | Rcpp, BH, RcppArmadillo |
| 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: | 2024-02-18 |
| 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 |
| Reference manual: | superml.pdf |
| Vignettes: |
Guide to CountVectorizer How to use TfidfVectorizer in R ? Introduction to SuperML |
| Package source: | superml_0.5.7.tar.gz |
| Windows binaries: | r-devel: superml_0.5.7.zip, r-release: superml_0.5.7.zip, r-oldrel: superml_0.5.7.zip |
| macOS binaries: | r-release (arm64): superml_0.5.7.tgz, r-oldrel (arm64): superml_0.5.7.tgz, r-release (x86_64): superml_0.5.7.tgz, r-oldrel (x86_64): superml_0.5.7.tgz |
| Old sources: | superml archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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