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This looks good - the only thing worth considering is whether instead of having a string 'evalmetric' as input into the model design we may want an evaluationMetricSetting created via a createEvalidationMetricSetting() that lets the user specify a function or a string name of a function and settings such as maximize (boolean). That way the package is more flexible to enable custom evaluation metrics. For backwards compatibility we should make the setting default to 'AUC' and maximize. Then we will need to add a table for this setting into the results tables - I'm happy to help with that part. |
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closing since this was merged in #618 |
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