The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
mlr3pipelines graph and PipeOp usage with
an internal mlr3 regression learner providing fitted preprocessing,
three-fold out-of-fold stacking, optional base-learner selection, and
averaging or learned stacking. The package no longer needs
mlr3pipelines to install, load, train or predict.mlr3viz, visNetwork,
tictoc, forcats, readr,
lubridate, purrr and Metrics
dependencies. Mapping, timing, regression metrics, categorical
processing and pipeline diagrams now use internal/base R code. R6 is now
declared directly for the internal mlr3 learner.mlr3filters to Suggests; correlation and variance
are internal. Specialized filters still use their original optional
backends, preserving their algorithms. Removed unused direct suggestions
of mlr3misc and nloptr.sense() arguments and result element
names. plot is now a sense_pipeline object,
drawn with plot(result$plot), rather than an HTML widget.
time_log is a base R difftime.
java_mem is accepted but unused. Stored model internals no
longer have the GraphLearner/PipeOp layout..Rnw)
to a knitr HTML vignette (.Rhtml), so building and checking
the package no longer requires a LaTeX installation or Ghostscript.NEWS.md file to track changes to the
package.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.