[ 源代码： r-cran-mlr ]
软件包：r-cran-mlr（2.13-1）
Machine learning in GNU R
Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.
其他与 r-cran-mlr 有关的软件包
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- dep: libc6 (>= 2.4)
- GNU C 语言运行库：共享库
同时作为一个虚包由这些包填实： libc6-udeb
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- dep: r-api-3.5
- 本虚包由这些包填实： r-base-core
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- dep: r-base-core (>= 3.5.1-1)
- GNU R core of statistical computation and graphics system
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- dep: r-cran-backports (>= 1.1.0)
- reimplementation of functions introduced since R-3.0.0
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- dep: r-cran-bbmisc (>= 1.11)
- GNU R Miscellaneous helper functions for B. Bischl
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- dep: r-cran-checkmate (>= 1.8.2)
- GNU R fast and versatile argument checks
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- dep: r-cran-data.table
- GNU R extension of Data.frame
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- dep: r-cran-ggplot2
- implementation of the Grammar of Graphics
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- dep: r-cran-parallelmap (>= 1.3)
- GNU R unified interface to parallelization back-ends
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- dep: r-cran-paramhelpers (>= 1.10)
- GNU R helpers for parameters in black-box optimization and tuning
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- dep: r-cran-stringi
- GNU R character string processing facilities
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- dep: r-cran-survival
- GNU R package for survival analysis
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- dep: r-cran-xml
- GNU R package for XML parsing and generation
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- sug: r-cran-batchtools
- GNU R tools for computation on batch systems
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- sug: r-cran-caret (>= 6.0-57)
- GNU R package for classification and regression training
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- sug: r-cran-class
- GNU R package for classification
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- sug: r-cran-cluster
- GNU R package for cluster analysis by Rousseeuw et al
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- sug: r-cran-e1071
- GNU R package with miscellaneous functions of the Dept of Statisics (e1071)
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- sug: r-cran-earth
- GNU R multivariate adaptive regression splines
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- sug: r-cran-emoa
- GNU R evolutionary multiobjective optimization algorithms
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- sug: r-cran-fpc
- GNU R flexible procedures for clustering
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- sug: r-cran-gbm
- GNU R package providing Generalized Boosted Regression Models
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- sug: r-cran-glmnet
- Lasso and Elastic-Net Regularized Generalized Linear Models
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- sug: r-cran-hmisc
- GNU R miscellaneous functions by Frank Harrell
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- sug: r-cran-irace (>= 2.0)
- GNU R iterated racing for automatic algorithm configuration
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- sug: r-cran-kernlab
- GNU R package for kernel-based machine learning lab
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- sug: r-cran-knitr
- GNU R package for dynamic report generation using Literate Programming
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- sug: r-cran-mass
- GNU R package of Venables and Ripley's MASS
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- sug: r-cran-mda
- GNU R mixture and flexible discriminant analysis
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- sug: r-cran-mlbench
- GNU R Machine Learning Benchmark Problems
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- sug: r-cran-modeltools
- GNU R package providing a collection of tools to deal with statistical models
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- sug: r-cran-nnet
- GNU R package for feed-forward neural networks
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- sug: r-cran-numderiv
- GNU R package for accurate numerical derivatives
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- sug: r-cran-pls
- GNU R partial least squares and principal component regression
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- sug: r-cran-purrr
- GNU R functional programming tools
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- sug: r-cran-randomforest
- GNU R package implementing the random forest classificator
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- sug: r-cran-ranger (>= 0.8.0)
- Fast Implementation of Random Forests
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- sug: r-cran-rgenoud
- R Version of GENetic Optimization Using Derivatives
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- sug: r-cran-rmarkdown
- convert R markdown documents into a variety of formats
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- sug: r-cran-rocr
- GNU R package to prepare and display ROC curves
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- sug: r-cran-rpart
- GNU R package for recursive partitioning and regression trees
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- sug: r-cran-sf
- Simple Features for R
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- sug: r-cran-svglite
- GNU R 'SVG' graphics device
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- sug: r-cran-testthat
- GNU R testsuite
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- sug: r-cran-tgp
- GNU R Bayesian treed Gaussian process models
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- sug: r-cran-th.data
- GNU R package for datasets by Torsten Hothorn