Package: superml 0.5.7

Manish Saraswat

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.

Authors:Manish Saraswat [aut, cre]

superml_0.5.7.tar.gz
superml_0.5.7.tar.gz(r-4.5-noble)superml_0.5.7.tar.gz(r-4.4-noble)
superml_0.5.7.tgz(r-4.4-emscripten)superml_0.5.7.tgz(r-4.3-emscripten)
superml.pdf |superml.html
superml/json (API)
NEWS

# Install 'superml' in R:
install.packages('superml', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/saraswatmks/superml/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

4.22 score 110 scripts 529 downloads 22 exports 7 dependencies

Last updated 10 months agofrom:9a7ac179b1. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-linux-x86_64NOTENov 15 2024

Exports:bm_25check_packageCounterCountVectorizerdotdotmatGridSearchCVkFoldMeanKMeansTrainerKNNTrainerLabelEncoderLMTrainerNBTrainernormalise1dnormalise2dRandomSearchCVRFTrainersmoothMeansort_indextestdataTfIdfVectorizerXGBTrainer

Dependencies:assertthatBHdata.tableMetricsR6RcppRcppArmadillo

How to use CountVectorizer in R ?

Rendered fromGuide-to-CountVectorizer.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2020-02-24
Started: 2020-02-19

How to use TfidfVectorizer in R ?

Rendered fromGuide-to-TfidfVectorizer.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-02-19
Started: 2020-02-19

Introduction to SuperML

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-02-19
Started: 2018-12-30