A False Positive Rate is a metric that can be used to assess machine learning accuracy. A model must have some notion of “ground reality,” or the true state of things, in order to get a reading on its true accuracy. The accuracy of models can then be directly evaluated by comparing their outputs to the ground reality.
This is most common with supervised learning methods, where the ground truth is a set of labels that classify and identify the underlying data. Classification is an example of supervised learning in which the labels are a distinct collection of classes that classify individual data points.
Based on what it has learned about historical data, the classifier will predict the most likely class for new data. Since the data is completely labeled, the expected value can be compared to the actual label (the ground truth) to determine the model’s accuracy.
There are four criteria for any given outcome in binary prediction/classification terminology:
When evaluating model accuracy, the True Positive Rate (TPR) and the False Positive Rate (FPR) are the two most common metrics to consider (FPR). The TPR, also known as “Sensitivity,” is a measure of how many positive cases in a set of data are correctly classified as such.
The FPR is the percentage of negative cases in the data that were mistakenly reported as positive (i.e. the probability that false alerts will be raised). The total number of negative cases wrongly reported as positive cases divided by the total number of negative cases (i.e. normal data) is described in this formula:
Machine learning uses the path of an algorithm to create a mathematical model as a starting point from training results. This enables a computer to make predictions without having to be specifically programmed to do so. The more data a computer has to “learn” from, the more precise it becomes.
This technology can be used to distinguish between regular consumer spending and transactions that may be linked to a fraudulent fee.
Thanks to the growth of mobile payments and the growing demand for a better customer experience, payments are one of the most digitized sections of the financial industry. Payments are vulnerable to digital fraud because they are so digitized.
Banks that want to retain businesses and draw prospects away from rivals want to give them the best experience possible. They do this by reducing the number of verification steps required to complete a transaction, reducing the efficacy of rule-based systems.
Since it can not only study current data and learn about consumer buying patterns but also consider the fluctuating nature of customer spending over the year, ML can fill in the gap (for example, travel at different times of the year, holiday spending, etc.).
As a result, ML has the potential to reduce the number of false positives detected by rules-based systems that are unable to discern anomalous but not inherently fraudulent conduct.
The false-positive rate in data science refers to the percentage of false positives in a binary classification problem compared to all positive predictions (the number of false positives and true positives). The false-positive rate is determined by the number of real negatives predicted incorrectly by the model.
The false-positive rate is one of several metrics used to assess how well it works when applied to classification models in machine learning.