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pmlbr  

Interface to the Penn Machine Learning Benchmarks Data Repository
View on CRAN: Click here


Download and install pmlbr package within the R console
Install from CRAN:
install.packages("pmlbr")

Install from Github:
library("remotes")
install_github("cran/pmlbr")

Install by package version:
library("remotes")
install_version("pmlbr", "0.3.0")



Attach the package and use:
library("pmlbr")
Maintained by
Trang Le
[Scholar Profile | Author Map]
First Published: 2020-10-02
Latest Update: 2020-10-02
Description:
Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository () contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository.
How to cite:
Trang Le (2020). pmlbr: Interface to the Penn Machine Learning Benchmarks Data Repository. R package version 0.3.0, https://cran.r-project.org/web/packages/pmlbr. Accessed 11 Apr. 2025.
Previous versions and publish date:
0.2.0 (2020-10-02 10:20), 0.2.1 (2023-09-29 01:40), 0.2.2 (2024-11-04 16:00), 0.2.3 (2025-02-20 00:40)
Other packages that cited pmlbr R package
View pmlbr citation profile
Other R packages that pmlbr depends, imports, suggests or enhances
Complete documentation for pmlbr
Functions, R codes and Examples using the pmlbr R package
Some associated functions: classification_dataset_names . compute_imbalance . dataset_names . fetch_data . get_type . nearest_datasets-methods . pmlb . pmlbr-package . regression_dataset_names . summary_stats . 
Some associated R codes: data.R . globals.R . nearest.R . pmlb.R . pmlbr-package.R .  Full pmlbr package functions and examples
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