What is false negative in ML
Artificial intelligence approaches are slowly but steadily becoming more common in many day-to-day corporate tasks. The ability of these approaches to create mathematical models of difficult, intuitive activities with remarkable precision has been highlighted in the media on several occasions.
Classification is one of the most common tasks for which AI is employed. The goal of this exercise is to sort numbers, photos, and text into a series of pre-defined categories. Consider an algorithm that can tell the difference between a human and an animal in a picture. This is a classifier. There are more practical and non-trivial applications for classifiers, such as detecting credit card fraud or ensuring that sensitive data is not stolen from an organization. The classifier’s goal in such jobs is to detect all possible incidents of fraud or data breach while ensuring that no real occurrence goes unnoticed.
The goal of AI is to guarantee that the computer detects all genuine positives while avoiding false positives. Organizations may face regulatory penalties as well as reputational harm if cases are not recognized by such systems. Despite the possible consequences, reducing and then entirely eliminating false negatives from classification results may be exceedingly challenging.
Reduce false negative
A good method of categorization in AI can assist cope with task anomalies. To eliminate false negatives, the successful strategy employs a cascade of models. The first layer searches for both positive and negative classes, whereas the second layer exclusively searches for negatives and any concealed positives. The following is a brief outline of the phases involved in the categorization process:
- Remodel – On the core functionality, do a non-linear modification. The examples from the original dataset that were difficult to categorize were labeled as positives in the previous phase. Given this reality, a non-linear modification can be used in the ensuing method to allow for better separation of the classes.
Dimensionality reduction methods can also be used to make the final model simpler. This allows for the creation of simpler models while without further complicating the flow.
- Start-up – Filter the principal classifier’s output to keep just the negatives, or valid, normal observations. This phase allows a portion of the dataset’s variance to be removed, perhaps resulting in simpler models and better learners.
From the original labels, create a new target. Positives represent the initial fake negatives, whereas negatives represent genuine negatives.
To get balanced datasets, use proper sampling strategies, as the original is likely to be quite unbalanced. The fraction of positive instances that the subsequent algorithm needs to learn (i.e. the initial false negatives) will be exceedingly low relative to the negatives due to the nature of the input dataset, i.e. a dataset generated as the output of a classifier (i.e. the original true negatives). This phase is necessary to ensure that the algorithm can properly learn.
Each of the possibilities outlined above has a distinct commercial value. Let’s stick with the model’s illustration of attempting to find future purchasers of consumers. The following are the probable business values of these variables:
- The contribution margin is a true positive. Because the model helped us find the proper customer and close the deal, all of the sale’s added value should be credited to it.
- False-positive: The contribution margin is negative. This may have been a sale, but the model misclassified it, preventing the sale from taking place. We are unable to complete this sale due to the model.
- There is no value in a true negative. Because no action was taken and no opportunity was missed, these data points are considered neutral.
- False-negative: The cost of marketing to contact the client, as well as the cost of frustration created by reaching her with an offer she doesn’t want. The latter expense is frequently underestimated. However, it is critical since reaching out to consumers needlessly causes them to unsubscribe or ignore our messages, reducing the likelihood of future transactions.
Models can also estimate their chances of being correct. This value is nearly as essential as the findings themselves since it allows your firm to strengthen its manual check/audit system or the business choices it makes based on the model. If the model is addressing a problem where people outperform the model, the refinement due to confidence levels is dependent on whether the model is tackling a problem where humans outperform the model:
- Consider adding extra controls for low confidence scenarios when people outperform the model at an acceptable cost in predictions. Using models in areas where people are superior may appear counterintuitive.
- Consider not following model predictions when the model’s confidence level is low if people cannot outperform the model – in areas like suggestion personalization or if the expense of human work is not worth the value created by a good choice – in big data solutions.