rtemis-package: 'rtemis': Machine Learning and Visualization
In egenn/rtemis: Machine Learning and Visualization
rtemis-package R Documentation
rtemis: Machine Learning and Visualization
Description
Advanced Machine Learning made easy, efficient, reproducible
Online Documentation and Vignettes
System Setup
There are some options you can define in your .Rprofile (usually found in your home directory),
so you do not have to define each time you execute a function.
- rt.theme
General plotting theme; set to e.g. "whiteigrid" or "darkgraygrid"
- rt.palette
Name of default palette to use in plots. See options by running rtpalette()
- rt.font
Font family to use in plots.
- rt.cores
Number of cores to use. By default, rtemis will use available cores reported by
future::availableCores(). In shared systems, you should limit this as appropriate.
- future.plan
Default plan to use for parallel processing.
Visualization
Graphics are handled using the draw family, which produces interactive plots usingplotly and
other packages.
Supervised Learning
Regression and Classification is performed using train().
This function allows you to preprocess, train, tune, and test models on multiple resamples.
Run available_supervised to get a list of available algorithms
Clustering
Clustering is performed using cluster().
Run available_clustering to get a list of available algorithms.
Decomposition
Decomposition is performed using decomp().
Run available_decomposition to get a list of available algorithms.
Notes
Function documentation includes input type (e.g. "String", "Integer",
"Float"/"Numeric", etc) and
range in interval notation where applicable. For example, Float: [0, 1)"
means floats between 0 and 1 including 0, but excluding 1
For all classification models, the outcome should be provided as a factor,
with the second level of the factor being the 'positive' class.
Author(s)
Maintainer: E.D. Gennatas gennatas@gmail.com (ORCID)
See Also
Useful links:
egenn/rtemis documentation built on June 14, 2025, 11:54 p.m.
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| rtemis-package | R Documentation |
rtemis: Machine Learning and Visualization
Description
Advanced Machine Learning made easy, efficient, reproducible
Online Documentation and Vignettes
System Setup
There are some options you can define in your .Rprofile (usually found in your home directory), so you do not have to define each time you execute a function.
- rt.theme
General plotting theme; set to e.g. "whiteigrid" or "darkgraygrid"
- rt.palette
Name of default palette to use in plots. See options by running
rtpalette()- rt.font
Font family to use in plots.
- rt.cores
Number of cores to use. By default, rtemis will use available cores reported by future::availableCores(). In shared systems, you should limit this as appropriate.
- future.plan
Default plan to use for parallel processing.
Visualization
Graphics are handled using the draw family, which produces interactive plots usingplotly and
other packages.
Supervised Learning
Regression and Classification is performed using train().
This function allows you to preprocess, train, tune, and test models on multiple resamples.
Run available_supervised to get a list of available algorithms
Clustering
Clustering is performed using cluster().
Run available_clustering to get a list of available algorithms.
Decomposition
Decomposition is performed using decomp().
Run available_decomposition to get a list of available algorithms.
Notes
Function documentation includes input type (e.g. "String", "Integer", "Float"/"Numeric", etc) and range in interval notation where applicable. For example, Float: [0, 1)" means floats between 0 and 1 including 0, but excluding 1
For all classification models, the outcome should be provided as a factor, with the second level of the factor being the 'positive' class.
Author(s)
Maintainer: E.D. Gennatas gennatas@gmail.com (ORCID)
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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