A developer is using machine learning to train a system that identifies fraudulent insurance claims. What is best suited for training data?

The most effective training data for a machine learning system designed to identify fraudulent insurance claims would consist of samples of known fraudulent and legitimate claims. This approach allows the model to learn the distinguishing features and patterns that differentiate legitimate claims from fraudulent ones.

By including both types of claims in the training dataset, the algorithm can create a more balanced representation of the claims it will encounter in real-life situations. It helps improve the model's accuracy and reduces bias that might arise from training exclusively on one type of claim.

This ensures that the machine learning model is equipped to recognize not only the characteristics commonly found in fraudulent claims but also the attributes of legitimate claims, enabling it to make more reliable predictions on new, unseen data. This dual representation in the dataset builds a comprehensive understanding of what constitutes fraud, enhancing the model's ability to effectively flag suspicious claims in the future.

In contrast, other options lack crucial elements necessary for effective model training. For instance, using only known legitimate claims or a new set of unknown claims would not provide the model with the essential information needed to identify fraud effectively.