How do you evaluate the performance of semi-supervised learning models for fraud detection?

1 Accuracy and recall

One of the simplest metrics to evaluate a model is accuracy, which is the proportion of correct predictions over the total number of predictions. However, accuracy can be misleading for fraud detection, where the data is highly imbalanced and the positive class (fraud) is rare. A model that predicts everything as negative (not fraud) can have a high accuracy, but a low ability to detect fraud. Therefore, another metric that is more relevant for fraud detection is recall, which is the proportion of true positives over the total number of positives. Recall measures how well the model can identify fraud cases among all the fraud cases.

2 Precision and F1-score

Another metric that is important for fraud detection is precision, which is the proportion of true positives over the total number of positive predictions. Precision measures how reliable the model is when it predicts fraud. A high precision means that the model has a low rate of false positives, which can reduce the cost and inconvenience of false alarms. However, precision and recall are often inversely related, meaning that improving one can lower the other. For example, a model that predicts everything as positive (fraud) can have a high recall, but a low precision. Therefore, a metric that combines both precision and recall is the F1-score, which is the harmonic mean of precision and recall. The F1-score can balance the trade-off between precision and recall and reflect the overall quality of the model.

3 AUC-ROC and AUC-PR

Another way to evaluate a model is to use a curve that plots the relationship between different metrics at different thresholds. A threshold is a value that determines whether a prediction is positive or negative based on the model's output probability. For example, a threshold of 0.5 means that any prediction with a probability higher than 0.5 is considered positive, and any prediction with a probability lower than 0.5 is considered negative. A curve that plots the recall against the false positive rate (FPR) at different thresholds is called the receiver operating characteristic (ROC) curve. The area under the ROC curve (AUC-ROC) measures how well the model can distinguish between fraud and not fraud across all thresholds. A higher AUC-ROC means a better model. A curve that plots the precision against the recall at different thresholds is called the precision-recall (PR) curve. The area under the PR curve (AUC-PR) measures how well the model can identify fraud among all the positive predictions across all thresholds. A higher AUC-PR means a better model.

4 Challenges and limitations

Evaluating semi-supervised learning models for fraud detection is not without challenges and limitations. One of the main challenges is the availability and quality of labeled data. Since labeled data is scarce and costly, it may not be representative of the true distribution of fraud and not fraud cases. This can lead to bias and variance in the model's performance. Another challenge is the choice of semi-supervised learning algorithm. There are different types of semi-supervised learning algorithms, such as self-training, co-training, and graph-based methods, that have different assumptions and properties. The performance of each algorithm may depend on the characteristics of the data, such as the amount and distribution of labeled and unlabeled data, the similarity and diversity of features, and the complexity and noise of the problem. Therefore, it is important to compare and test different algorithms and parameters to find the best fit for the data.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?