Model Training |
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Machine learning made easy |
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Tune multiple machine learning models using cross validation to optimize performance |
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Train models without tuning for performance |
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Prediction |
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Make predictions using the best-performing model |
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Model Interpretation |
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Get model performance metrics |
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Get variable importances |
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Visualization |
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Plot performance of models |
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Plot model predictions vs observed outcomes |
Plot variable importance |
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Plot missingness |
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Create a control chart |
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Data Preparation |
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Prepare data for machine learning |
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Impute data and return a reusable recipe |
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Split data into training and test data frames |
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Data Manipulation |
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Pivot multiple rows per observation to one row with multiple columns |
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Convert MSDRGs into a "base DRG" and complication level |
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Add SAM utility columns to table |
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Data Profiling |
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Find missingness in each column and search for strings that might represent missing values |
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Connect to Databases |
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Build a connection string for use with MSSQL and dbConnect |
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Read from a SQL Server database table |
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Logging |
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Sets console logging to a file in the working directory. |
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Stops all console logging. |
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Test function to check that the production environment is active. |
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Save and Load Models |
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Save models to disk and load models from disk |
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Example Data |
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Patient diabetes dataset |
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Model Details |
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Supported models and their hyperparameters |
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Get hyperparameter values |