👉 Cover math, often referred to as "coverage analysis," is a statistical method used to evaluate the effectiveness of a model, particularly in classification tasks. It measures how well a model's predictions cover the true outcomes across all possible classes or categories. The primary goal is to determine if the model's predictions are sufficiently comprehensive and representative of the data distribution. This is typically done by calculating metrics such as the coverage rate, which is the proportion of the true classes that the model's predictions include. Another key metric is the coverage ratio, which compares the number of true instances correctly predicted to the total number of instances in each class. By analyzing these metrics, cover math helps identify potential gaps or biases in the model's performance, guiding improvements to enhance overall accuracy and reliability.