Man pages for healthcareai
Tools for Healthcare Machine Learning
| add_best_levels | Build efficient features from high-cardinality,... |
| 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 |
| build_one_level_df | Replace all value in the column of a dataframe with a given... |
| catalyst_test_deploy_in_prod | Defunct |
| control_chart | Create a control chart |
| convert_date_cols | Convert character date columns to dates and times |
| db_read | Read from a SQL Server database table |
| evaluate | Get model performance metrics |
| evaluate_classification | Get performance metrics for classification predictions |
| evaluate_multiclass | Get performance metrics for multiclass predictions |
| evaluate_regression | Get performance metrics for regression predictions |
| explore | Explore a model's "reasoning" via counterfactual predictions |
| flash_models | Train models without tuning for performance |
| get_cutoffs | Get cutoff values for group predictions |
| get_hyperparameter_defaults | Get hyperparameter values |
| get_supported_models | Supported models and their hyperparameters |
| get_thresholds | Get class-separating thresholds for classification... |
| get_variable_importance | Get variable importances |
| hcai_impute | Specify imputation methods for an existing recipe |
| healthcareai | Machine Learning Made Easy |
| impute | Impute data and return a reusable recipe |
| interpret | Interpret a model via regularized coefficient estimates |
| is.model_list | Type checks |
| is.predicted_df | Class check |
| machine_learn | Machine learning made easy |
| make_na | Replace missingness values with NA and correct columns types |
| missingness | Find missingness in each column and search for strings that... |
| Mode | Mode |
| permute_process_variables | Take a dataframe and build a larger dataframe by permuting... |
| pima_diabetes | Patient diabetes dataset |
| pima_meds | Patient medications dataset |
| pip | Patient Impact Predictor |
| pivot | Pivot multiple rows per observation to one row with multiple... |
| plot.explore_df | Plot Counterfactual Predictions |
| plot.interpret | Plot regularized model coefficients |
| plot.missingness | Plot missingness |
| plot.model_list | Plot performance of models |
| plot.predicted_df | Plot model predictions vs observed outcomes |
| plot.thresholds_df | Plot threshold performance metrics |
| plot.variable_importance | Plot variable importance |
| predict.model_list | Get predictions |
| prep_data | Prepare data for machine learning |
| reexports | Objects exported from other packages |
| rename_with_counts | Adds the category count to each category name in a given... |
| save_models | Save models to disk and load models from disk |
| 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 | Defunct |
| step_add_levels | Add levels to nominal variables |
| step_date_hcai | Date and Time Feature Generator |
| step_dummy_hcai | Dummy Variables Creation |
| step_locfimpute | Last Observation Carried Forward Imputation |
| step_missing | Clean NA values from categorical/nominal variables |
| stop_prod_logs | Defunct |
| summary.missingness | Summarizes data given by 'missingness' |
| tune_models | Tune multiple machine learning models using cross validation... |
