rtemis-package: 'rtemis': Advanced Machine Learning and Visualization
In rtemis: Schema-Driven Machine Learning and Visualization
rtemis-package R Documentation
rtemis: Advanced Machine Learning and Visualization
Description
Schema-driven machine learning for validated, observable, auditable, and reproducible
machine learning workflows.
Online Documentation and Vignettes
Visualization
Model plots (plot_true_pred(), plot_roc(), plot_varimp(), plot_metric(),
plot_learning(), plot_session(), plot_manhattan()) and present() render
interactive charts through 'rtemis.draw', which also provides the draw_*() functions for
data. draw_linad() draws a LINAD tree.
Supervised Learning
By convention, the last column of the data is the outcome variable, and all other columns are
predictors. Convenience function set_outcome can be used to move a specified column to the
end of the data.
Regression and Classification is performed using train().
This function allows you to preprocess, train, tune, and test models on multiple resamples.
Use available_supervised to get a list of available algorithms
Classification
For training of binary classification models, the outcome should be provided as a factor,
with the second level of the factor being the 'positive' class.
Clustering
Clustering is performed using cluster().
Use available_clustering to get a list of available algorithms.
Decomposition
Decomposition is performed using decomp().
Use available_decomposition to get a list of available algorithms.
Type Documentation
Function documentation includes input type (e.g. "Character", "Integer",
"Float"/"Numeric", etc).
When applicable, value ranges are provided in interval notation. For example, Float: [0, 1)
means floats between 0 and 1 including 0, but excluding 1.
Categorical variables may include set of allowed values using curly braces.
For example, Character: {"future", "mirai", "none"}.
Tabular Data
rtemis internally uses methods for efficient handling of tabular data, with support for
data.frame, data.table, and tibble. If a function is documented as accepting
"tabular data", it should work with any of these data structures. If a function is documented
as accepting only one of these, then it should only be used with that structure.
For example, some optimized data.table operations that perform in-place modifications only
work with data.table objects.
Error Conditions
Errors are signalled as classed conditions that can be handled with
tryCatch(). See rtemis_conditions for the class hierarchy.
Author(s)
Maintainer: E.D. Gennatas gennatas@gmail.com (ORCID) [copyright holder]
Authors:
E.D. Gennatas gennatas@gmail.com (ORCID) [copyright holder]
See Also
Useful links:
rtemis documentation built on Oct. 20, 2026, 1:06 a.m.
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| rtemis-package | R Documentation |
rtemis: Advanced Machine Learning and Visualization
Description
Schema-driven machine learning for validated, observable, auditable, and reproducible machine learning workflows.
Online Documentation and Vignettes
Visualization
Model plots (plot_true_pred(), plot_roc(), plot_varimp(), plot_metric(),
plot_learning(), plot_session(), plot_manhattan()) and present() render
interactive charts through 'rtemis.draw', which also provides the draw_*() functions for
data. draw_linad() draws a LINAD tree.
Supervised Learning
By convention, the last column of the data is the outcome variable, and all other columns are
predictors. Convenience function set_outcome can be used to move a specified column to the
end of the data.
Regression and Classification is performed using train().
This function allows you to preprocess, train, tune, and test models on multiple resamples.
Use available_supervised to get a list of available algorithms
Classification
For training of binary classification models, the outcome should be provided as a factor, with the second level of the factor being the 'positive' class.
Clustering
Clustering is performed using cluster().
Use available_clustering to get a list of available algorithms.
Decomposition
Decomposition is performed using decomp().
Use available_decomposition to get a list of available algorithms.
Type Documentation
Function documentation includes input type (e.g. "Character", "Integer", "Float"/"Numeric", etc). When applicable, value ranges are provided in interval notation. For example, Float: [0, 1) means floats between 0 and 1 including 0, but excluding 1. Categorical variables may include set of allowed values using curly braces. For example, Character: {"future", "mirai", "none"}.
Tabular Data
rtemis internally uses methods for efficient handling of tabular data, with support for
data.frame, data.table, and tibble. If a function is documented as accepting
"tabular data", it should work with any of these data structures. If a function is documented
as accepting only one of these, then it should only be used with that structure.
For example, some optimized data.table operations that perform in-place modifications only
work with data.table objects.
Error Conditions
Errors are signalled as classed conditions that can be handled with
tryCatch(). See rtemis_conditions for the class hierarchy.
Author(s)
Maintainer: E.D. Gennatas gennatas@gmail.com (ORCID) [copyright holder]
Authors:
E.D. Gennatas gennatas@gmail.com (ORCID) [copyright holder]
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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