How should you determine the accuracy of a machine learning model created in Oracle Analytics Cloud?

Determining the accuracy of a machine learning model involves evaluating its performance across various metrics that give insight into how well the model makes predictions. The F1 score is a crucial metric in this context, as it balances both precision and recall, providing a single score that reflects the model's accuracy in classifying cases correctly.

By opening the Inspect dialog and reviewing the F1 score, one is able to comprehensively assess the model's ability to avoid false positives and false negatives. This score is particularly important in scenarios where there is an uneven class distribution, making it more informative than simply relying on accuracy alone. A high F1 score indicates a well-performing model that effectively identifies relevant instances and minimizes errors in prediction.

In contrast, running the model with various filter values, while useful for testing performance under different conditions, does not provide a standardized measurement of accuracy. Creating a data flow that includes a histogram may help visualize the distribution of data but does not directly assess model performance. Similarly, using the debug option in the data flow is more related to troubleshooting and understanding the data processing rather than measuring the model’s accuracy directly. Thus, focusing on the F1 score is the most effective way to determine the accuracy of the machine learning model.