Target Drift

Target drift refers to changes in the distribution of the target variable over time, which can affect the performance of machine learning models.

Basic concepts: 

• Baseline: Users can define the baseline basis on ‘Tag’ or segment of data based on ‘date’.
• ‍Frequency: Users can define how frequently they want to calculate the monitoring metrics
• ‍Alerts frequency: Users can configure how frequently they want to be notified about the alerts

Data drift and Target drift have similar ways to configuring and running the alerts. The settings are different.

To fetch the default target drift dashboard, use the following command:

project.get_target_drift_dashboard()

To create a new target drift dashboard:

project.get_target_drift_dashboard(payload)

You can also use the help function to get all parameters and payloads

help(project.get_target_drift_dashboard)

Drift Metrics: AryaXAI offers various statistical tests to analyze target drift, including the Chi-square test, Jensen-Shannon distance, Kolmogorov-Smirnov (K-S) test, Kullback-Leibler Divergence, Population Stability index (Psi), Wasserstein distance, and Z-test.

Available Statistical tests:

You can learn more about these tests in our wiki section.