Скачать или смотреть Understanding the Different Kinds of Wrong in Machine Learning Predictions

In machine learning, there are different ways to be wrong, such as false positives and false negatives. Each type of error carries different costs and implications. For instance, in marketing, the cost of missing a potential sale (false negative) can be far greater than mistakenly targeting someone who won’t buy (false positive). Understanding these trade-offs is essential, and data scientists must fine-tune models based on the specific context of the decision at hand.With curated, governed, access to real-time data, you can better manage error trade-offs and focus on refining predictive models, ultimately leading to smarter business decisions.

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