shinyML 1.0.1 (2021-02-21)
New features
- Histogram plots on
shiny_h2o and
shiny_spark functions now integrate density curves.
shiny_h2o and shiny_spark functions ensure
reproducibility of results when user reproduce the same
parameters for a given machine learning model
shiny_h2o and shiny_spark functions now
work with an input dataset that contains a POSIXct
column
shinyML 1.0.0 (2020-10-02)
New features
shiny_h2o and shiny_spark functions have
merged into shinyML_regression function: H2O or Spark can
now be chosen just using the framework argument.
- A new function
shinyML_classification has been
implemented to train and test machine learning models for
classification tasks : classification results can be
viewed through confusion matrix charts in addition to existing available
item on old package versions .
- When the framework is set to H2O for
shinyML_regression or shinyML_classification
function, authorized model families for auto ML searching can be
manually specified.
- Two new info cards have been set on the upper part
to precise the type of machine learning task (regression or
classification) and the dimension of input dataset.
- Autocorrelation plots are now available for
numerical variables on Variable summary tab
Breaking changes
- User interface completely changed on shiny apps for both
shinyML_regression and shinyML_classification
functions : argonDash and argonR shiny API
have been used to make user experience even more friendly.
- Both
shinyML_regression and
shinyML_classification automatically detect if
input dataset contains a time-based column: in that case,
training and testing dataset splitting is done in order to respect
chronology. On the other case, rows are randomly assigned to training or
testing dataset according to a splitting percentage parameter.
shinyML 0.2.0 (2019-10-28)
New features
- Informations about cluster memory and number of used CPU(s) are now
available on the left side when running
shiny_h2o and
shiny_h2o functions
- Three new tabs are now available at the top of the
shiny_h2o and shiny_h2o dashboards to explore
input data set. The Variable Summary tab allows to
check types and box plot of each input variable. The Explore
dataset tab gives the possibility to understand dependencies by
plotting each data variable as a function of another. An overview of all
variables dependencies is also available in the Correlation
matrix tab.
Breaking changes
- Output tabs like Compare models performances,
Feature importance and Table of
results are now hidden when no model has been running. It
showed a message indicating that output couldn’t be calculated because
no model was trained.
- x input parameter of
shiny_h2o and
shiny_h2o have been removed to give even more simplicity
for the user: the dashboard now indicates at the top right of the
dashboard which input variable are available to train the model (output
variable y is automatically removed from the list).
Bug fixes
- Intercept term button for Generalized linear
regression model has changed on both functions due to problem
on the UI: the cursor was not at the right position when selected.
- Link button of Generalized linear regression model
doesn’t have any effect on the output variable due to omission to take
this parameter in account. This issue has been fixed.
shinyML 0.1.1 (2019-08-07)
Bug fixes
- autoML method is now working on
shiny_h2o function: the
user now just need to set maximum calculation time.
Breaking changes
- Default
share_app argument of shiny_h2o
and shiny_spark examples have been set to FALSE.