ML Model Drift

Model drift, also known as Model decay is changes in the performance or behavior of a machine learning( ML ) model over time due to changes in the underlying data distribution or relationships between inputs and outputs. It can result in degraded performance and incorrect predictions if not addressed effectively.

Types of Model Drift

Different Types of model drift are as follows:

Concept Drift

Occurs when the relationship between input features and the target variable changes over time.
Example: In fraud detection, fraudsters may change their tactics, altering the patterns the model learned.

Data Drift

Happens when the distribution of input features changes over time.
Example: A model trained on customer data from a specific region may perform poorly if applied to data from a different region.

Covariate Shift

A specific form of data drift where the distribution of the independent variables changes, but the relationship between inputs and outputs remains the same.

Prior Probability Shift

Occurs when the distribution of the target variable changes, even if the relationship between inputs and outputs remains stable. For example: A sudden increase in the number of fraudulent transactions during a specific season.

Causes of Model Drift

Real-world changes: Business processes, user behaviors, or external factors (e.g., economic or environmental changes). Incomplete or biased training data: When the training data doesn’t fully represent future scenarios. Time decay: Gradual changes in patterns or relationships over time.

Detecting Model Drift

You can follow different steps to detect the model drift. Some of them are as follows:

Monitoring Metrics

Regularly evaluate model performance using validation datasets.
Compare current metrics (accuracy, precision, recall, etc.) against historical baselines.

Statistical Tests

Use statistical methods to compare the distributions of current data with historical data (e.g., KS test, Chi-squared test).

Visualization

Plot distributions of key features or outputs over time to identify shifts.

Handling Model Drift

The different method to handle the model drift are as follows:

• Retraining: Update the model periodically with the latest data to account for changes.
• Online Learning: Use models that can adapt to new data in real-time.
• Ensemble Methods: Use ensemble techniques to combine multiple models, some of which are trained on newer data.
• Feedback Loops: Incorporate user feedback to refine and update the model.
• Drift Detection Systems: Implement automated systems to monitor and alert when significant drift is detected.

By proactively monitoring and addressing model drift, machine learning systems can maintain robust performance over time.