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Chapter 16: Understanding the Process (16.1 Learning Curves and Speed…

- - - - he performance of the Elastic-Net model can be addressed more simply at the three data levels (16%, 32%, 64%).
        
        When considering the addition of more data, it is important to calculate the cost, if, that is, such data is available. If the data available is data from an earlier date than what is currently owned, it is not clear that the data will lead to improvements, as data gets “stale” over time. As the size of the dataset increases, cross validation becomes a better indicator.
- - - - Always look for the efficient frontier line. If time is a factor, the efficient frontier will need to be followed to the left until the most efficient model is found that is still reasonably accurate.
- - - - The process also most likely uses the most important information from the three text features (diag_1_desc, diag_2_desc, and diag_3_desc) after the texts have been processed by the Auto-Tuned Word N-Gram Text Modeler
        
        16.3.1 Imputation
        
        Regularized Logistic Regression model: blueprint contains more information. In this case, categorical features are one-hot encoded, and numerical features have their missing values imputed. The imputation uses the median value for the feature
        
        indicator feature will simply contain a False if that row was not imputed and a True if a given row contains a value that was imputed
        
        16.3.2 Standardization
        
        Standardize: After imputing age and standardization of the feature, only the imputed and standardized feature is used by the Regularized Logistic Regression algorithm.
        
        16.3.3 One-hot Encoding
        
        for any categorical feature that fulfills certain requirements, a new feature is created for every category that exists within the original feature