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
Static graphics are handled using the mplot3 family.
Dynamic graphics are handled using the dplot3 family.
Supervised Learning
Functions for Regression and Classification begin with s_*.
Run select_learn to get a list of available algorithms
The documentation of each supervised learning function indicates in
brackets, after the title whether the function supports classification,
regression, and survival analysis [C, R, S]
Clustering
Functions for Clustering begin with c_*.
Run select_clust to get a list of available algorithms
Decomposition
Functions for Decomposition and Dimensionality reduction begin with
d_*.
Run select_decom to get a list of available algorithms
Cross-Decomposition
Functions for Cross-Decomposition begin with x_*.
Run xselect_decom to get a list of available algorithms
Meta-Modeling
Meta models are trained using meta* functions.
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 first level of the factor being the 'positive' class, if
applicable. A character vector supplied as outcome will be converted to
factors, where by default the levels are set alphabetically and therefore
the positive class may not be set correctly.
Author(s)
Maintainer: E.D. Gennatas gennatas@gmail.com (ORCID)
See Also
Useful links:
egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| 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
Static graphics are handled using the mplot3 family.
Dynamic graphics are handled using the dplot3 family.
Supervised Learning
Functions for Regression and Classification begin with s_*.
Run select_learn to get a list of available algorithms
The documentation of each supervised learning function indicates in
brackets, after the title whether the function supports classification,
regression, and survival analysis [C, R, S]
Clustering
Functions for Clustering begin with c_*.
Run select_clust to get a list of available algorithms
Decomposition
Functions for Decomposition and Dimensionality reduction begin with
d_*.
Run select_decom to get a list of available algorithms
Cross-Decomposition
Functions for Cross-Decomposition begin with x_*.
Run xselect_decom to get a list of available algorithms
Meta-Modeling
Meta models are trained using meta* functions.
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 first level of the factor being the 'positive' class, if applicable. A character vector supplied as outcome will be converted to factors, where by default the levels are set alphabetically and therefore the positive class may not be set correctly.
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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