kms() accepts a number of parameters (like loss and optimizer) and splits the data into (optionally sparse) test and training matrices. kms() facilitates setting advanced hyperparameters (e.g., regularization). kms() returns a single object with predictions, a confusion matrix, and function call details.

person("Turgut", "Abdullayev", role = c("aut", "cre"),
person(family = "Google Inc.", role = c("cph"))
Description: 'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans.
It aims at making the life of AI practitioners, hypertuner
algorithm creators and model designers as simple as possible by
providing them with a clean and easy to use API for hypertuning.
'Keras Tuner' makes moving from a base model to a hypertuned one quick and
easy by only requiring you to change a few lines of code.
SystemRequirements: TensorFlow >= 2.0 (https://www.tensorflow.org/)
Imports: reticulate, tensorflow, rstudioapi, plotly, data.table,
Suggests: knitr, rmarkdown, covr, stats, testthat, keras, tfdatasets,
Packaged: 2020-03-15 12:10:36 UTC; turgutabdullayev

RJSONIO, rjson, tidyjson, dplyr, echarts4r, magick, crayon,
Suggests: knitr, rmarkdown, tfdatasets, testthat, purrr
Packaged: 2020-05-14 07:53:08 UTC; turgutabdullayev

RJSONIO, rjson, tidyjson, dplyr, echarts4r, crayon, keras,
Suggests: knitr, tfdatasets, testthat, purrr, rmarkdown

Package: kerdiest
Type: Package
Title: Nonparametric kernel estimation of the distribution function.
Bandwidth selection and estimation of related functions.
Version: 1.0
Date: 2011-02-18
Author: Alejandro Quintela del Rio and Graciela Estevez Perez
Maintainer: Alejandro Quintela del Rio <aquintela@udc.es>
Description: Nonparametric kernel distribution function estimation is
performed. Three automatic bandwidth selection methods for
nonparametric kernel distribution function estimation are
implemented: the plug-in of Altman and Leger, the plug-in of
Polansky and Baker, and the modified cross-validation of
Bowman, Hall and Prvan. The exceedence function, the mean
return period and the return level are also computed.
License: GPL (>= 2)
LazyLoad: yes
Depends: date, chron
Packaged: 2011-03-08 18:51:50 UTC; winxp
Repository: CRAN
Date/Publication: 2011-03-09 14:25:07