Showing 10 of total 10 results (show query)
openml
OpenML:Open Machine Learning and Open Data Platform
We provide an R interface to 'OpenML.org' which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. See <https://www.openml.org/guide/api> for more information.
Maintained by Giuseppe Casalicchio. Last updated 10 months ago.
arffbenchmarkingbenchmarking-suiteclassificationdata-sciencedatabasedatasetdatasetsmachine-learningmachine-learning-algorithmsopen-dataopen-scienceopendataopenmlopenscienceregressionreproducible-researchstatistics
11.0 match 97 stars 11.04 score 7.1k scripts
cran
foreign:Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ...
Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.
Maintained by R Core Team. Last updated 2 months ago.
8.3 match 6 stars 8.59 score 913 dependents
kurthornik
RWeka:R/Weka Interface
An R interface to Weka (Version 3.9.3). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Package 'RWeka' contains the interface code, the Weka jar is in a separate package 'RWekajars'. For more information on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.
Maintained by Kurt Hornik. Last updated 2 years ago.
8.3 match 4 stars 8.24 score 1.8k scripts 14 dependents
gesistsa
rio:A Swiss-Army Knife for Data I/O
Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data format from the file extension, reasonable defaults are used for data import and export, web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly, and fast import packages are used where appropriate. An additional convenience function, 'convert()', provides a simple method for converting between file types.
Maintained by Chung-hong Chan. Last updated 3 months ago.
csvcsvydatadata-scienceexcelioriosasspssstata
2.2 match 605 stars 17.08 score 7.8k scripts 71 dependents
fcharte
mldr:Exploratory Data Analysis and Manipulation of Multi-Label Data Sets
Exploratory data analysis and manipulation functions for multi- label data sets along with an interactive Shiny application to ease their use.
Maintained by David Charte. Last updated 5 years ago.
4.3 match 23 stars 7.07 score 168 scripts 2 dependents
mlr-org
mlr3oml:Connector Between 'mlr3' and 'OpenML'
Provides an interface to 'OpenML.org' to list and download machine learning data, tasks and experiments. The 'OpenML' objects can be automatically converted to 'mlr3' objects. For a more sophisticated interface with more upload options, see the 'OpenML' package.
Maintained by Sebastian Fischer. Last updated 9 months ago.
datadata-sciencedatasetsmachine-learningmlr3openmlcpp
4.0 match 6 stars 5.37 score 105 scriptscran
rmcfs:The MCFS-ID Algorithm for Feature Selection and Interdependency Discovery
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.
Maintained by Michal Draminski. Last updated 7 months ago.
4.3 match 1 stars 2.95 score 1 dependents
madr0008
mldr.resampling:Resampling Algorithms for Multi-Label Datasets
Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets.
Maintained by Miguel Ángel Dávila. Last updated 1 years ago.
3.2 match 1 stars 2.70 score 7 scripts
agvico
SDEFSR:Subgroup Discovery with Evolutionary Fuzzy Systems
Implementation of evolutionary fuzzy systems for the data mining task called "subgroup discovery". In particular, the algorithms presented in this package are: M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) <doi:10.1109/TFUZZ.2006.890662> M. J. del Jesus, P. Gonzalez, F. Herrera (2007) <doi:10.1109/MCDM.2007.369416> C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200> C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) <doi:10.1016/j.ins.2014.11.030> It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in KEEL, ARFF and CSV format and also with data.frame objects.
Maintained by Angel M. Garcia. Last updated 4 years ago.
2.2 match 2.53 score 34 scripts