Version 4.06 of the caret package was sent to CRAN.
caret can be used to tune the parameters of predictive models using
resampling, estimate variable importance and visualize the results.
There are also various modeling and "helper" functions that can be
useful for training models. caret has wrappers to over 50 different
models for classification and regression. See the package vignettes or
the paper at
http://www.jstatsoft.org/v28/i05
for more details.
Significant internal changes were made to how the models are fit in
train(). Now, the function used to compute the models is passed in as
a parameter (defaulting to lapply). In this way, users can use their
own parallel processing software without new versions of caret.
Examples using MPI and NWS are given in ?train.
The package now contains a function (splsda) that extends the spls
function to classification (in the same manner than caret's plsda
function extends plsr).
Also, fixed a bug where the MSE (instead of RMSE) was reported for
random forest OOB resampling
There are more examples in ?train.
Changes to confusionMatrix, sensitivity, specificity and the
predicative value functions:
- each was made more generic with default and table methods
- confusionMatrix "extractor" functions for matrices and tables were
added
- the pos/neg predicted value computations were changed to
incorporate prevalence
- prevalence was added as an option to several functions
- detection rate and prevalence statistics were added to confusionMatrix
- the examples were expanded in the help files
This version of caret will break compatibility with caretLSF and
caretNWS. However, these packages will not be needed now (see above)
and will be deprecated. They will work on versions of caret <= 3.51
and will not be developed going forward. However, they can still be
found at
fhttps://r-forge.r-project.org/projects/caret/
Send questions, comments etc to max.kuhn at pfizer.com.
Max
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