Introduction to False Positive and False Negative

Classification algorithms predict the future outcome of specific events using existing data.

But are these predictions accurate?

How can we determine the accuracy of these predictable models?

Customers churn / customer retention refers to the loss of customers over a period of time. Separation algorithms, such as retrieval items can help a business predict whether a customer will stay or not.

But the results of such algorithms are never completely accurate, and they have some degree of uncertainty / potential attached to them.

Business conditions with only two effects are known as binary conditions.

For example, whether the customer will renew their subscription at the end of the year, or if the customer will respond positively to the marketing campaign.

Such situations can be easily portrayed as a 4x4 matrix, with each result labeled as follows:

True and negative real cells represent the accuracy of speculative models used to determine future conditions.

The values ​​in these cells show that the effect is exactly the same as predicted by the retrieval model, and can help us determine the reliability of the model.

The results of the false cells and the false cells show the opposite effects of the predictions made by the prediction model.

Any situation with binary results will always have the potential to have negative and negative positive consequences. According to the business application, the business leader must determine whether it is acceptable to have more false than false or vice versa.

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