A confusion matrix is a tool used to evaluate the performance of a search or classification system. It shows how the retrieved documents are classified in terms of relevance. The matrix consists of four components: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). True positives are relevant documents that were correctly retrieved, while false positives are irrelevant documents that were incorrectly retrieved.
In the context of information retrieval (IR), a confusion matrix can help identify how well a system distinguishes between relevant and irrelevant documents. For example, a high number of false positives may indicate that the system is retrieving too many irrelevant documents. This provides a basis for further improving the ranking algorithm.
By calculating metrics such as precision, recall, and F1 score from the confusion matrix, developers can assess the system's overall performance. This is useful in iterating and fine-tuning IR systems to deliver more relevant and accurate results.