rtemis-package: 'rtemis': Machine Learning and Visualization

In egenn/rtemis: Machine Learning and Visualization

rtemis: Machine Learning and Visualization

Description

Advanced Machine Learning made easy, efficient, reproducible

Online Documentation and Vignettes

https://rtemis.org

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 [e-mail] (ORCID)

See Also

Useful links:

• https://rtemis.org

• Report bugs at https://github.com/egenn/rtemis/issues

egenn/rtemis documentation built on Nov. 22, 2024, 4:12 a.m.