Finding the balance between reducing false positives and increasing true positives

The most important feature of a predictive analytics platform is the generation of high-quality prediction models that have the best ratios of true and false positives. No model is perfect, but many organisations compromise by implementing models that lean towards over-detecting (disproportionate false positives) or under-detecting (few false positives but many missed true detections). This is key for optimising business processes as finding a good balance through high quality prediction models will lead to improved efficiencies in business processes, improved data-driven decision making, increased accuracy in forecasts and predictions, better understanding of market requirements leading to innovation and a clear competitive advantage.

The better the quality of a predictive model, the better the detection rate and the ratio of true to false positives. Adjusting the model to improve the detection rate without answering the business question precisely, is a common mistake, and therefore it is important that the integrity of the business question is maintained. Deciding on a final detection rate also requires understanding of the cost and benefits of each type of prediction in your specific context and making a decision that aligns with your requirements, goals and priorities.

In general, the trade-off between true positives and false positives can be managed by adjusting the threshold for making a positive prediction. A higher threshold will result in fewer positive predictions (both true and false), while a lower threshold will result in more positive predictions (both true and false). Some who implement models manually, use a confusion matrix that can provide visualisation of the model’s performance related to where the model is making errors or where it is being accurate.

Another approach is to use evaluation metrics that take both true positives and false positives into account, such as precision and recall, and optimise for a trade-off between the two. Precision is the ratio of true positives to the total number of positive predictions, while recall is the ratio of true positives to the total number of actual positive instances in the data. Depending on your specific needs, you may want to prioritise precision or recall, or find a balance between the two.

The optimal detection rate for predictive analytics models depends on the clear understanding of the problem domain, the nature of the data, and the level of accuracy required for the intended use case. In general, a higher detection rate indicates that the model is better at identifying relevant patterns or anomalies in the data. However, it's important to keep in mind that there is always a trade-off between detection rate and other performance metrics, such as precision, recall, and false positive rate, as this will ultimately reflect in cost and benefit considerations. Ultimately, trying to create a high quality prediction model is not a simple task, and organisations should turn to platforms with a strong track record of implementing models with a high detection quality.