Provides an R Interface to the 'FuzzyCoCo' C++ Library and Extends It
computes the optimal fuzzy set positions based on the distribution of ...
evaluate the fuzzy system from a fit on some given data
evaluate the fuzzy system from a fit on some given data
model parameters and data for the IRIS36 classification example
model parameters and data for the IRIS36 classification example
model parameters and data for the mtcars regression example
a one-row overview of a fuzzy system with the usage of variables, the ...
fit the FuzzyCoco model using the dataframe interface
fit the FuzzyCoco model using the formula interface
format the fuzzy rules as a data frame
extract the usage of the variables by a fuzzy system
this is an utility function used to implement the parsnip interface
parsnip model function
systematic search
lowest-level implementation of the fitting of a fuzzy coco model using...
creates a model for the Fuzzy Coco algorithm
utility to build the Fuzzy Coco parameters data structure
predict the outcome of a fuzzy system on some input data
predict the outcome on some input data using a fitted model
Objects exported from other packages
Rfuzzycoco: Provides an R Interface to the 'FuzzyCoCo' C++ Library and...
shared params
an utility function to easily generate a stop function that stops when...
an utility function to easily generate the commonly used until param...
Provides and extends the 'Fuzzy Coco' algorithm by wrapping the 'FuzzyCoCo' 'C++' Library, cf <https://github.com/Lonza-RND-Data-Science/fuzzycoco>. 'Fuzzy Coco' constructs systems that predict the outcome of a human decision-making process while providing an understandable explanation of a possible reasoning leading to it. The constructed fuzzy systems are composed of rules and linguistic variables. This package provides a 'S3' classic interface (fit_xy()/fit()/predict()/evaluate()) and a 'tidymodels'/'parsnip' interface, a custom engine with custom iteration stop criterion and progress bar support as well as a systematic implementation that do not rely on genetic programming but rather explore all possible combinations.
Useful links