MachineShop

Machine Learning Models and Tools

Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.

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Description file content

Package
MachineShop
Type
Package
Title
Machine Learning Models and Tools
Version
2.0.0
Date
2019-12-09
Author
Brian J Smith [aut, cre]
Maintainer
Brian J Smith
Description
Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.
Depends
R (>= 3.6.0)
Imports
abind, dials (>= 0.0.4), foreach, ggplot2, kernlab, magrittr, methods, party, polspline, recipes (>= 0.1.4), rlang, rsample, Rsolnp, survival, tibble, utils
Suggests
adabag, BART, bartMachine, C50, doParallel, e1071, earth, gbm, glmnet, gridExtra, Hmisc, kableExtra, kknn, knitr, lars, mda, MASS, mboost, nnet, partykit, pls, randomForest, ranger, rmarkdown, rms, rpart, testthat, tree, xgboost
LazyData
true
License
GPL-3
URL
BugReports
https://github.com/brian-j-smith/MachineShop/issues
RoxygenNote
7.0.2
VignetteBuilder
knitr
NeedsCompilation
no
Packaged
2019-12-10 15:03:54 UTC; bjsmith
Repository
CRAN
Date/Publication
2019-12-10 22:40:07 UTC

install.packages('MachineShop')

2.0.0

5 days ago

https://brian-j-smith.github.io/MachineShop/

Brian J Smith

GPL-3

Depends on

R (>= 3.6.0)

Imports

abind, dials (>= 0.0.4), foreach, ggplot2, kernlab, magrittr, methods, party, polspline, recipes (>= 0.1.4), rlang, rsample, Rsolnp, survival, tibble, utils

Suggests

adabag, BART, bartMachine, C50, doParallel, e1071, earth, gbm, glmnet, gridExtra, Hmisc, kableExtra, kknn, knitr, lars, mda, MASS, mboost, nnet, partykit, pls, randomForest, ranger, rmarkdown, rms, rpart, testthat, tree, xgboost

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