How can you evaluate the performance of a machine learning model in Oracle Analytics Cloud?

Evaluating the performance of a machine learning model in Oracle Analytics Cloud can be effectively achieved by connecting the model directly inside a project. This approach allows users to leverage the model's predictions and insights seamlessly within their analysis, providing a straightforward means to assess the model's performance based on real data.

By integrating the model into a project, users can examine its output against actual outcomes, enabling them to evaluate metrics such as accuracy, precision, recall, or any other relevant performance indicators. This hands-on interaction with the model's predictions in the context of their data will offer insights into how well the model generalizes to new data, thus facilitating a comprehensive evaluation of its performance.

In contrast, other methods might not directly allow for performance evaluation. For example, utilizing a data flow with a Commit Model step is typically focused on saving or deploying trained models rather than assessing their effectiveness. Similarly, creating a custom calculation may provide additional statistical analysis but does not inherently involve directly evaluating the model's outputs against actual results. Embedding the model into a data flow sequence may assist in certain workflows, yet it lacks the direct evaluation capabilities that connecting the model inside a project offers.