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.
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.
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
risk_groups argument to
plot support for outcome- and risk-group predictions.
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
missingness finds any such strings it issues a warning with code that can be used to do the replacement.
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.
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.
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
machine_learn was used).
remove_near_zero_variance argument of
NA for complication when the DRG is missing.
methods is attached on attaching the package so that scripts operate the same in Rscript, R GUI, and R Studio.
A whole new architecture featuring a simpler API, more rigor under the hood, and attractive plots.
methods to maintain functionality across environments
LassoDeployment for usage details
skip_on_not_appveyor will skip a unit test unless it’s being run on Appveyor.
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 training
SupervisedModelDeployment no longer trains models. It only loads the model saved in
SupervisedModelDevelopment. Predictions are made for all data.
imputeColumn was replaced with
DBI backend. We support reading and writing to MSSQL and SQLite databases.
getPredictions() in development (lasso, random forest, linear mixed model)