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

To determine the accuracy of a machine learning model in Oracle Analytics Cloud, reviewing the F1 score through the Inspect dialog is crucial. The F1 score is a performance metric that combines precision and recall, providing a single score that reflects the model's accuracy in classification tasks. This metric is particularly valuable when working with imbalanced datasets, as it takes both false positives and false negatives into account, offering a more nuanced understanding of the model's effectiveness.

By accessing the Inspect dialog, users can not only view the F1 score but also gain insights into other relevant performance metrics, allowing for a comprehensive evaluation of the model's predictive capabilities. This facilitates informed decisions about model adjustments or deployments based on quantitative performance measures, essential for discerning how well the model is likely to perform in real-world scenarios.

In contrast, filtering values, creating histograms, or debug options may provide contextual or technical insights into the data or modeling process but do not directly quantify the model's accuracy in the same way that the F1 score does.