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| healthcareai-package | Machine Learning Made Easy |
| add_SAM_utility_cols | Add SAM utility columns to table |
| as.model_list | Make models into model_list object |
| build_connection_string | Build a connection string for use with MSSQL and dbConnect |
| catalyst_test_deploy_in_prod | Test function to check that the production environment is active. |
| control_chart | Create a control chart |
| convert_date_cols | Convert character date columns to dates |
| countMissingData | Function to find proportion of NAs in each column of a dataframe or matrix |
| db_read | Read from a SQL Server database table |
| evaluate | Get model performance metrics |
| evaluate.model_list | Get model performance metrics |
| evaluate.predicted_df | Get model performance metrics |
| evaluate_classification | Get performance metrics for classification predictions |
| evaluate_regression | Get performance metrics for regression predictions |
| flash_models | Train models without tuning for performance |
| get_hyperparameter_defaults | Get hyperparameter values |
| get_random_hyperparameters | Get hyperparameter values |
| get_supported_models | Supported models and their hyperparameters |
| get_variable_importance | Get variable importances |
| hcai_impute | Specify imputation methods for an existing recipe |
| healthcareai | Machine Learning Made Easy |
| hyperparameters | Get hyperparameter values |
| impute | Impute data and return a reusable recipe |
| is.classification_list | Type checks |
| is.model_list | Type checks |
| is.predicted_df | Class check |
| is.regression_list | Type checks |
| load_models | Save models to disk and load models from disk |
| machine_learn | Machine learning made easy |
| missingness | Find missingness in each column and search for strings that might represent missing values |
| models | Supported models and their hyperparameters |
| models_supported | Supported models and their hyperparameters |
| pima_diabetes | Patient diabetes dataset |
| pivot | Pivot multiple rows per observation to one row with multiple columns |
| plot.missingness | Plot missingness |
| plot.model_list | Plot performance of models |
| plot.predicted_df | Plot model predictions vs observed outcomes |
| plot.variable_importance | Plot variable importance |
| plot_classification_predictions | Plot model predictions vs observed outcomes |
| plot_regression_predictions | Plot model predictions vs observed outcomes |
| predict.model_list | Make predictions using the best-performing model |
| prep_data | Prepare data for machine learning |
| save_models | Save models to disk and load models from disk |
| selectData | Defunct. See 'db_read' |
| separate_drgs | Convert MSDRGs into a "base DRG" and complication level |
| split_train_test | Split data into training and test data frames |
| start_prod_logs | Sets console logging to a file in the working directory. |
| step_add_levels | Add levels to nominal variables |
| step_date_hcai | Date Feature Generator |
| step_missing | Clean NA values from categorical/nominal variables |
| stop_prod_logs | Stops all console logging. |
| supported_models | Supported models and their hyperparameters |
| tidy.step_add_levels | Add levels to nominal variables |
| tidy.step_date_hcai | Date Feature Generator |
| tune_models | Tune multiple machine learning models using cross validation to optimize performance |
| writeData | Defunct. See this vignette for help writing to databases. |