{"id":8716,"date":"2020-04-06T10:00:00","date_gmt":"2020-04-06T17:00:00","guid":{"rendered":""},"modified":"2025-06-11T07:59:37","modified_gmt":"2025-06-11T14:59:37","slug":"ai-builder-new-prediction-model-performance-page","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/power-platform\/blog\/power-apps\/ai-builder-new-prediction-model-performance-page\/","title":{"rendered":"AI Builder – New prediction model performance page"},"content":{"rendered":"

We\u2019ve updated the prediction model performance page in AI Builder. Now, there\u2019s a performance grade to help give you a better idea of how the model is performing. We\u2019ve also added a new performance tab on the training details pane to provide more detailed information to help you evaluate performance. \u00a0These improvements help you decide when a model is ready to publish. In this post, we will discuss how to use the new measurements.<\/p>\n

Why this change<\/h1>\n

We\u2019ve received customer feedback that the accuracy score alone wasn\u2019t sufficient to help make publishing decisions. \u00a0Some people were confused by the performance score.<\/p>\n

Let\u2019s look at an example model that predicts business loan approvals. \u00a0Here are 2 contrasting scenarios that have the same accuracy score (71%), but vastly different practical performance:<\/p>\n\n\n\n\n\n
Business loan approval model<\/td>\nHistorical approval rate<\/td>\nPerformance score<\/td>\nAccuracy grade<\/td>\n<\/tr>\n
Scenario 1<\/td>\n50%<\/td>\n71%<\/td>\nB<\/td>\n<\/tr>\n
Scenario 2<\/td>\n70%<\/td>\n71%<\/td>\nC<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The model has a 71% accuracy rate, but scenario 1 represents a significant improvement, while scenario 2 is only marginally better than a random guess. Therefore, the accuracy score alone isn\u2019t helpful, but the accuracy grade (A, B, C, D) based on \u00a0improvement relative to a random guess can help you decide if your model is ready to use.<\/p>\n

Existing models<\/h1>\n

For any existing trained or model, you won\u2019t see the new performance information until you retrain it.<\/p>\n

Performance grade<\/h1>\n

After you train your model, the performance grade appears on the model details page. Accuracy may vary based on the data you use, but the grade is more consistent for a given model.<\/p>\n

\"\"<\/p>\n

\u00a0<\/p>\n

Let\u2019s say your model predicts whether a business loan is approved or denied. If your model grade is B, it means the model performance is generally good. \u00a0You have to decide whether it is good enough based on your own unique circumstances. Does it have room to improve? Yes \u2013 probably. You can follow instructions in the AI Builder documentation<\/a> to keep improving your model if you want to achieve better accuracy.<\/p>\n

More information about what each grade means and how they\u2019re calculated: Prediction model performance.<\/a><\/p>\n

Training details performance tab<\/h1>\n

In addition to training summary, we\u2019ve introduced a new Performance<\/strong> tab in the training details pane that appears when you click see details<\/strong> below the performance grade. The following performance metrics are shown:<\/p>\n