**A line graph. Title: Performance Metrics Comparison . On the X-axis title: Performance Metrics. Along the x-axis metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). Each metric should be clearly labeled along the x-axis. Y-axis title: Metric Values. The y-axis will indicate the magnitude or level of performance achieved by the AI algorithms. The scale on the y-axis should be appropriately chosen to accommodate the range of values for the metrics being compared. It should be clearly labeled to indicate the metric values or scores. By plotting the different performance metrics on the x-axis and their respective values or scores on the y-axis, the figure will provide a visual comparison of the performance of AI algorithms across multiple metrics. This allows for an evaluation of their effectiveness in different aspects, providing insights into their relative strengths and weaknesses.** - Image #2 <@761305052005924964>
A line graph. Title Performance Metrics Comparison . On the Xaxis title Performance Metrics. Along the xaxis metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve AUCROC. Each metric should be clearly labeled along the xaxis. Yaxis title Metric Values. The yaxis will indicate the magnitude or level of performance achieved by the AI algorithms. The scale on the yaxis should be appropriately chosen to accommodate the range of values for the metrics being compared. It should be clearly labeled to indicate the metric values or scores. By plotting the different performance metrics on the xaxis and their respective values or scores on the yaxis, the figure will provide a visual comparison of the performance of AI algorithms across multiple metrics. This allows for an evaluation of their effectiveness in different aspects, providing insights into their relative strengths and weaknesses.