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Documentation is refactored using Roxygen2 and considerably enhanced.
All camelCase function names now have their equivalence in
snake_case, e.g., mlRforest ->
ml_rforest(), or confusionImage() ->
confusion_image() in order to adapt to the coding
preferences of the user.
mlRpart() function implements
rpart::rpart() for using decision trees.The description is extended.
A {pkgdown} site is added.
mlKnn() is implemented for K-nearest
neighbors.
Several adjustments were required for compatibility with R 4.2.0 (it is not allowed any more to use vectors > 1 with || and &&).
predict() was applied to an mlearning object build
with full formula (not the short one var ~ .), if the
dependent variable was not in newdata =, an error message
was raised (although this variable is not necessary at this point). Bug
identified by Damien Dumont, and corrected.mlSvm.formula(), arguments scale=,
type=, kernel= and classwt= were
not correctly used. Corrected.mlLvq() providing size = or
prior = led to an lvq object not found
message. Corrected.Sometimes, data was not found (e.g., when called inside a {learnr} tutorial).
In mlearning(), data is forced with
as.data.frame() (tibbles are not supported
internally).
In the mlXXX() function, it was not possible to
indicate something like mlLda(data = iris, Species ~ .).
Solved by adding train = argument in
mlXXX().
In summary.confusion() produced an error if more
than one type = was provided.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.