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.
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