healthcareai now depends on dplyr 1.0.0 and tibble
3.0.0. You will need these versions or later. Various hidden changes
were made to be compatible with these packages’ lastest
breaking changes.allow_parallel parameter was removed to prevent
nested parallelism.healthcareai now depends on recipes 0.1.4 and caret
6.0.81. You will need these versions or later. Various hidden changes
were made to be compatible with these packages’ lastest
breaking changes.bagimpute in prep_data now accepts
bag_trees to specify the number of trees. This is updated
to be compatible with recipes
0.1.4.healthcareai library versions now are
saved to model objects.explore. Make
counterfactual predictions across the most-important features in a model
to see how those features influence predicted outcomes.
plot method to visualize a model’s logic.pip. Carefully specify variables and
alternative values that exert causal influence on outcomes; then get
recommended actions for a given patient with expected outcomes given the
actions.outcome_groups argument to
predict).risk_groups argument to predict.
plot support for outcome- and risk-group
predictions.get_thresholds.
plot method to compare performance across metrics at
various thresholds.split_train_test can keep multiple observations of an
individual in the same split via the grouping_col
argument.NA with make_na.
missingness finds any such strings it issues a
warning with code that can be used to do the replacement.rename_with_counts.summary.missingness method for wide datasets with
missingness in many columns.prep_data, trigonometric transformations make
circular features out of dates and times for more informative features
in less-wide data frames.plot.model_class and
summary.model_class.machine_learn.missingness is faster.add_best_levels works in deployment even if none of the
columns to be created are present in the deployment observations.prep_data can handle logical features.outcome doesn’t need to be re-declared in model
training if it was specified in data prep.caret-trained models into a
model_list.add_best_levels and get_best_levels.interpret and plot.interpret to extract
glmnet estimates.variable_importance returns random forest or xgboost
importances, whichever model performs better.predict can now write an extensive log file, and if
that option is activated, as in production, predict is a
safe function that always completes; if there is an error, it returns a
zero-row data frame that is otherwise the same as what would have been
returned (provided prep_data or machine_learn
was used).remove_near_zero_variance argument of
prep_data.separate_drgs returns NA for complication
when the DRG is missing.model_list
objects.methods is attached on attaching the package so that
scripts operate the same in Rscript, R GUI, and R Studio.ggplot2,
broom, and recipes.A whole new architecture featuring a simpler API, more rigor under the hood, and attractive plots.
ranger and
caretmethods to maintain functionality across
environmentsgetProcessVariablesDfRandomForestDeployment and LassoDeployment for
usage detailsskip_on_not_appveyor will skip a unit test unless it’s
being run on Appveyor.skip_if_no_mssql isn’t needed as a test utility
anymore.XGBoostDevelopment and XGBoostDeployment.KmeansClustering.findVariaion will return groups with the highest
variation of a chosen target measure within a data set.variationAcrossGroups will plot a boxplot of variation
between groups for a chosen target measure.SupervisedModelDevelopment now saves the model after
trainingSupervisedModelDeployment no longer trains models. It
only loads the model saved in SupervisedModelDevelopment.
Predictions are made for all data.imputeColumn was replaced with
imputeDFDBI backend. We support reading and
writing to MSSQL and SQLite databases.testWindowCol is no longer a param in
SupervisedModelDeployment or used in the algorithms.writeToDB is no longer a param in
SupervisedModelDeployment or used in the algorithms.destSchemaTable is no longer a param in
SupervisedModelDeployment or used in the algorithms.getPredictions() in
development (lasso, random forest, linear mixed model)