Function reference • healthcareai

Model Training
`machine_learn()`	Machine learning made easy
`tune_models()`	Tune multiple machine learning models using cross validation to optimize performance
`flash_models()`	Train models without tuning for performance
Prediction
`predict(<model_list>)`	Get predictions
`pip()`	Patient Impact Predictor
`get_thresholds()`	Get class-separating thresholds for classification predictions
Model Interpretation
`explore()`	Explore a model's "reasoning" via counterfactual predictions
`get_variable_importance()`	Get variable importances
`interpret()`	Interpret a model via regularized coefficient estimates
`evaluate()`	Get model performance metrics
Visualization
`plot(<predicted_df>)` `plot_regression_predictions()` `plot_classification_predictions()` `plot_multiclass_predictions()`	Plot model predictions vs observed outcomes
`plot(<model_list>)`	Plot performance of models
`plot(<explore_df>)`	Plot Counterfactual Predictions
`plot(<variable_importance>)`	Plot variable importance
`plot(<interpret>)`	Plot regularized model coefficients
`plot(<missingness>)`	Plot missingness
`plot(<thresholds_df>)`	Plot threshold performance metrics
`control_chart()`	Create a control chart
Data Preparation
`add_best_levels()` `get_best_levels()`	Build efficient features from high-cardinality, multiple-membership factors
`prep_data()`	Prepare data for machine learning
`split_train_test()`	Split data into training and test data frames
`impute()`	Impute data and return a reusable recipe
Data Manipulation
`pivot()`	Pivot multiple rows per observation to one row with multiple columns
`separate_drgs()`	Convert MSDRGs into a "base DRG" and complication level
`add_SAM_utility_cols()`	Add SAM utility columns to table
`rename_with_counts()`	Adds the category count to each category name in a given variable column
`make_na()`	Replace missingness values with NA and correct columns types
Data Profiling
`missingness()`	Find missingness in each column and search for strings that might represent missing values
Connect to Databases
`build_connection_string()`	Build a connection string for use with MSSQL and dbConnect
`db_read()`	Read from a SQL Server database table
Save and Load Models
`save_models()` `load_models()`	Save models to disk and load models from disk
Example Data
`pima_diabetes`	Patient diabetes dataset
`pima_meds`	Patient medications dataset
Model Details
`get_supported_models()`	Supported models and their hyperparameters
`get_hyperparameter_defaults()` `get_random_hyperparameters()`	Get hyperparameter values

Reference

Model Training

Prediction

Model Interpretation

Visualization

Data Preparation

Data Manipulation

Data Profiling

Connect to Databases

Save and Load Models

Example Data

Model Details