The Accuracy Paradox

Today, let me try and convince you to avoid the accuracy metric in favor of other metrics such as precision and recall.

Accuracy is often the starting point for analyzing the quality of a predictive model. Accuracy is also probably the first term that comes to mind when non-experts think about how to evaluate the quality of a prediction. As shown below, accuracy measures the ratio of correct predictions over the total number of cases evaluated.

What about the business relevance of accuracy? Surprisingly, this is a difficult question. It seems obvious that the ratio of correct predictions over all cases should be a key metric for determining the business impact of a predictive model. Yet, the value of the accuracy metric is dubious. In fact, it is often trivially easy to create a predictive model with high accuracy, and such trivial models can be useless despite of high accuracy. Similarly, when comparing the business impact of two alternative predictive models, it may well be the less accurate model that is more beneficial to the user organization.

Let's review an example predictive model for an insurance fraud application. To prevent payment on fraudulent claims all cases that are predicted as high-risk by the model will be investigated by fraud experts. The insurance has devised a predictive model that predicts fraud with some degree of accuracy. And in order to evaluate the performance of the model the insurance has created a sample data set of 10,000 claims. All 10,000 cases in the validation sample have been carefully checked and it is known which cases are fraudulent. Now, to analyze the quality of the model, the insurance uses the table of confusion below.

	Predicted Negative	Predicted Positive
Negative Cases	9,700	150
Positive Cases	50	100

Table 1: Table of Confusion for Fraud Model M₁^Fraud.

The accuracy for model M₁^Fraud computes to:



With an accuracy of 98.0% model M₁^Fraud appears to perform fairly well. However, the Accuracy Paradox lies in the fact that accuracy can be easily improved to 98.5% by always predicting "no fraud". The table of confusion and the accuracy for this trivial “always predict negative” model M₂^Fraud are shown below.

	Predicted Negative	Predicted Positive
Negative Cases	9,850	0
Positive Cases	150	0

Table 1: Table of Confusion for Fraud Model M₂^Fraud.



Model M₂^Fraud reduces the rate of inaccurate predictions from 2% to 1.5%. This is an apparent improvement of 25%. Although the new model M₂^Fraud shows fewer incorrect predictions and markedly improved accuracy, as compared to the original model M₁^Fraud, the new model is obviously useless. The alternative model M₂^Fraud does not offer any value to the insurance company for preventing fraud, and clearly, the less accurate model is more useful than the more accurate model. The inescapable conclusion is that high accuracy is not necessarily an indicator of high model quality, and therein lies the Accuracy Paradox of predictive analytics. High accuracy does not necessarily lead to desirable business outcomes, and model improvements should not be measured in terms of accuracy gains. It may go too far to say that accuracy is irrelevant for assessing business benefits but I advise against using accuracy when evaluating predictive models.

Posted at 03:40PM Jan 16, 2006 by tilmannsblog in The Predictive Business  |  Comments[0]