We all know false positives in machine learning can be costly. And while we also know that high quality data is imperative to the success of your algorithm, in some cases, data quality is even more critical than others. For example, a false positive in an autonomous vehicle or biomedical algorithm could mean life or death, however, in the case of an e-commerce chatbot, it may just result in poor customer service. Since the weight and severity of a false positive differs across verticals, it’s important to define the level of data quality and domain expertise needed to train your algorithm, as a part of your training data strategy.
The Cost of a False Positive
