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:
superml_0.5.7.tar.gz
superml_0.5.7.zip(r-4.5)superml_0.5.7.zip(r-4.4)superml_0.5.7.zip(r-4.3)
superml_0.5.7.tgz(r-4.5-x86_64)superml_0.5.7.tgz(r-4.5-arm64)superml_0.5.7.tgz(r-4.4-x86_64)superml_0.5.7.tgz(r-4.4-arm64)superml_0.5.7.tgz(r-4.3-x86_64)superml_0.5.7.tgz(r-4.3-arm64)
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://saraswatmks.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/saraswatmks/superml/issues
Last updated 1 years agofrom:0d7f6aea09. Checks:8 OK, 4 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 14 2025 |
| R-4.5-win-x86_64 | NOTE | Mar 14 2025 |
| R-4.5-mac-x86_64 | NOTE | Mar 14 2025 |
| R-4.5-mac-aarch64 | NOTE | Mar 14 2025 |
| R-4.5-linux-x86_64 | NOTE | Mar 14 2025 |
| R-4.4-win-x86_64 | OK | Mar 14 2025 |
| R-4.4-mac-x86_64 | OK | Mar 14 2025 |
| R-4.4-mac-aarch64 | OK | Mar 14 2025 |
| R-4.4-linux-x86_64 | OK | Mar 14 2025 |
| R-4.3-win-x86_64 | OK | Mar 14 2025 |
| R-4.3-mac-x86_64 | OK | Mar 14 2025 |
| R-4.3-mac-aarch64 | OK | Mar 14 2025 |
Exports:bm_25check_packageCounterCountVectorizerdotdotmatGridSearchCVkFoldMeanKMeansTrainerKNNTrainerLabelEncoderLMTrainerNBTrainernormalise1dnormalise2dRandomSearchCVRFTrainersmoothMeansort_indextestdataTfIdfVectorizerXGBTrainer
Dependencies:assertthatBHdata.tableMetricsR6RcppRcppArmadillo
How to use CountVectorizer in R ?
Rendered fromGuide-to-CountVectorizer.Rmdusingknitr::rmarkdownon Mar 14 2025.Last update: 2020-02-23
Started: 2020-02-16
How to use TfidfVectorizer in R ?
Rendered fromGuide-to-TfidfVectorizer.Rmdusingknitr::rmarkdownon Mar 14 2025.Last update: 2024-02-18
Started: 2020-02-16
Introduction to SuperML
Rendered fromintroduction.Rmdusingknitr::rmarkdownon Mar 14 2025.Last update: 2024-02-18
Started: 2018-12-17
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| BM25 Matching | bm_25 |
| cla_train | cla_train |
| Calculate count of values in a list or vector | Counter |
| Count Vectorizer | CountVectorizer |
| Dot product similarity in vectors | dot |
| Dot product similarity between a vector and matrix | dotmat |
| Grid Search CV | GridSearchCV |
| kFoldMean Calculator | kFoldMean |
| K-Means Trainer | KMeansTrainer |
| K Nearest Neighbours Trainer | KNNTrainer |
| Label Encoder | LabelEncoder |
| Linear Models Trainer | LMTrainer |
| Naive Bayes Trainer | NBTrainer |
| normalise1d | normalise1d |
| normalise2d | normalise2d |
| Random Search CV | RandomSearchCV |
| reg_train | reg_train |
| Random Forest Trainer | RFTrainer |
| smoothMean Calculator | smoothMean |
| sort_index | sort_index |
| TfIDF(Term Frequency Inverse Document Frequency) Vectorizer | TfIdfVectorizer |
| Extreme Gradient Boosting Trainer | XGBTrainer |