rtemis: Machine Learning and Visualization
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.
General plotting theme; set to e.g. "light" or "dark"
Plotting theme for true vs. fitted; set to e.g. "lightgrid" or "darkgrid"
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.
Static graphics are handled using the mplot3 family.
Dynamic graphics are handled using the dplot3 family.
Functions for Regression and Classification begin with s.*.
Run modSelect 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]
Functions for Clustering begin with u.*.
Run clustSelect to get a list of available algorithms
Functions for Decomposition and Dimensionality reduction begin with d.*.
Run decomSelect to get a list of available algorithms
Functions for Cross-Decomposition begin with x.*.
Run xdecomSelect to get a list of available algorithms
Meta models are trained using meta* functions.
Function documentation includes input type (e.g. "String", "Integer", "Float", 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.