HealthCatalyst/healthcareai-r: Snowmass Mountain

Published June 29, 2018 | Version v2.1.0

Software Open

Added

Identify values of high-cardinality variables that will make good features, even with multiple values per observation with add_best_levels and get_best_levels.
glmnet for regularized linear and logistic regression.
interpret and plot.interpret to extract glmnet estimates.
XGBoost for regression and classification models.
variable_importance returns random forest or xgboost importances, whichever model performs better.

Changed

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).
Control how low variance must be to remove columns by providing a numeric value to the remove_near_zero_variance argument of prep_data.
Fixed bug in missingness that caused very small values to round to zero.
Messages about time required for model training are improved.
separate_drgs returns NA for complication when the DRG is missing.
Removed some redundent training data from model_list objects.
methods is attached on attaching the package so that scripts operate the same in Rscript, R GUI, and R Studio.
Minor changes to maintain compatibility with ggplot2, broom, and recipes.

Removed

Files

Name	Size	Download all
HealthCatalyst/healthcareai-r-v2.1.0.zip md5:1d96e6ba63627ffb23187fd97a059bc7	3.4 MB	Preview Download

Is supplement to: https://github.com/HealthCatalyst/healthcareai-r/tree/v2.1.0 (URL)