There are various theoretical approaches to measuring accuracy* of competing machine learning models however, in most commercial applications, you simply need to assign a business value to 4 types of results: true positives, true negatives, false positives and false negatives. By multiplying number of results in each bucket with the associated business values, you will ensure that you use the best model available.
Further complicating this situation is the confidence vales provided by the model. Almost all machine learning models can be built to provide a level of confidence for their answer. A high level approach to using this value in accuracy* measurement is to multiply it with the results, essentially rewarding the model for providing high confidence values for its correct assessments.