Introduction

The "Observations" section serves as a powerful tool for assessing the correlation between industry knowledge and model performance. It enables subject matter experts to contribute to the explainability process by providing clear and understandable explanations to all stakeholders.

In this section, users can explore the rationale behind each prediction. By defining specific conditions or causes as "observations," users can establish a correlation between these factors and the model's predictions. This functionality facilitates a deeper understanding of causation correlations within the model's decision-making process.

Create Observations

To create a new observation, navigate to the 'Observations' tab in ML Explainability.

Select the ‘Create observation’ button on the right. Assign a name to the observation and utilize the drag-and-drop feature to add the expression node. Next, specify the feature (data point) for which you intend to create the observation. Choose from conditional operators such as 'not equal to,' 'equal to,' 'greater than,' or 'less than,' and input the desired feature value.

Once the operation is defined, select the linked features from the dropdown menu on the left. You can select multiple features here and write an observation statement.

Manage Observations

Once saved, all observations are listed in the observations tab under 'Observation List', with creation and updation details. Users can manage observations from here.

The advanced view option under ‘Options’ column in the list provides additional details, such as who updated the observation, observation statement, linked features and the expression. 

If any of the observations hold true for a case, it will be displayed below the case. This can be viewed at ML explainability > View cases > ‘View’ under the ‘Options’ column in the summary table.

Selecting the ‘Advanced view’ option provides additional details on the observations. The ‘Success’ column here displays whether the particular observation is running on the. ‘Triggered’ will show if the observation is relevant to the current case.

Observations score

Observation score is the sum of feature importance of linked features.

Observation trail

In the Observations section, all changes made to observations are systematically logged and can be accessed through the Observations Trail.

This section presents a structured table containing essential details such as the initial creation date, subsequent updates, their corresponding dates and times, and the current status of each observation.

Furthermore, the 'Options' feature within the table provides a 'Show' functionality, allowing users to access both the Current and Old Configuration data. This feature offers detailed insights into the modifications, including the user responsible for the update, the specific statement that underwent changes, the features impacted by the modification, and the exact alterations made. This comprehensive display facilitates a thorough examination of the modification history, ensuring transparency and accountability in the observation tracking process.