gafs_initial {caret} | R Documentation |
Built-in functions related to genetic algorithms
gafs_initial(vars, popSize, ...) gafs_lrSelection(population, fitness, r = NULL, q = NULL, ...) gafs_rwSelection(population, fitness, ...) gafs_tourSelection(population, fitness, k = 3, ...) gafs_spCrossover(population, fitness, parents, ...) gafs_uCrossover(population, parents, ...) gafs_raMutation(population, parent, ...) caretGA rfGA treebagGA
vars |
number of possible predictors |
popSize |
the population size passed into |
population |
a binary matrix of the current subsets with predictors in columns and individuals in rows |
fitness |
a vector of fitness values |
parent, parents |
integer(s) for which chromosomes are altered |
r, q, k |
tuning parameters for the specific selection operator |
... |
not currently used |
These functions are used with the functions
argument of the gafsControl
function. More information on the details of these functions are at http://topepo.github.io/caret/GA.html.
Most of the gafs_*
functions are based on those from the GA package by Luca Scrucca. These functions here are small re-writes to work outside of the GA package.
The objects caretGA
, rfGA
and treebagGA
are example lists that can be used with the functions
argument of gafsControl
.
In the case of caretGA
, the ...
structure of gafs
passes through to the model fitting routine. As a consequence, the train
function can easily be accessed by passing important arguments belonging to train
to gafs
. See the examples below. By default, using caretGA
will used the resampled performance estimates produced by train
as the internal estimate of fitness.
For rfGA
and treebagGA
, the randomForest
and bagging
functions are used directly (i.e. train
is not used). Arguments to either of these functions can also be passed to them though the gafs
call (see examples below). For these two functions, the internal fitness is estimated using the out-of-bag estimates naturally produced by those functions. While faster, this limits the user to accuracy or Kappa (for classification) and RMSE and R-squared (for regression).
The return value depends on the function.
Luca Scrucca, gafs_initial
, caretGA
, rfGA
and treebagGA
by Max Kuhn
Scrucca L (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37.
cran.r-project.org/web/packages/GA/
http://topepo.github.io/caret/GA.html
pop <- gafs_initial(vars = 10, popSize = 10) pop gafs_lrSelection(population = pop, fitness = 1:10) gafs_spCrossover(population = pop, fitness = 1:10, parents = 1:2) ## Not run: ## Hypothetical examples lda_ga <- gafs(x = predictors, y = classes, gafsControl = gafsControl(functions = caretGA), ## now pass arguments to `train` method = "lda", metric = "Accuracy" trControl = trainControl(method = "cv", classProbs = TRUE)) rf_ga <- gafs(x = predictors, y = classes, gafsControl = gafsControl(functions = rfGA), ## these are arguments to `randomForest` ntree = 1000, importance = TRUE) ## End(Not run)