Model behavior is explained by global or local interpretations and corresponds to features, target predictions, and learning from segments.
Why is Model Behavior Important?
ML models are rigorously tested in training to ensure they behave as accurately as possible. However, once the models are deployed in production, the model behavior keeps fluctuating. These fluctuations are usually caused by changing data or changing data sources. If these fluctuations are unchecked, the model behavior changes, and the output or prediction of the model will be impacted. This impact is generally observed over a longer duration of time.
To maintain the model behavior and capture the fluctuations at their inception, it is necessary to thoroughly understand the model’s decision-making. This is where Machine Learning Explainability comes into the picture.
Machine learning explainability brings the right approach to explain the reasons behind any specific prediction made by the ML model. It helps understand and interpret the model’s behavior. For example:
- Which features are important for predicting?
- What is the relationship between the input features and the target predictions?
- Did the model learn anything unexpected?
- Does the model specialize or learn something from a specific training data segment?
- Does the model generalize?
Approaches to Explaining ML Model Behavior
ML model behavior is explained using multiple approaches.
Global interpretation: It represents the overall or global explanation of model behavior. The global approach demonstrates a big-picture view of the model and how different features affect the overall result.
Local interpretation: The local approach to model behavior considers each instance in the data and individual features that affect model predictions.
Intrinsic or post-hoc interpretability: These approaches consider applying interpretation methods before or after model training, respectively.
Model-specific or model-agnostic: Model-specific interpretation tools serve a single model or group of models. Contrarily, the model-agnostic tool applies to any ML model. Agnostic methods usually work by analyzing the input feature and output pair.
A Better Way of Keeping Model Behavior in Check
Machine learning explainability drives better accountability, trust, performance improvement, and control within AI systems. Insights on ML model behavior help define a roadmap for improving ML models in production and ensure a transparent and explicable ML system.
Censius reinvents model behavior and activity monitoring with its flagship AI Observability platform. Its activity monitors look out for any anomalous behavior by models, potential future trends, and the possibility of model overload.
With an AI Observability Platform like Censius, any irregularities in model behavior are detected in real-time, and the user will be notified accordingly. Such tools free up the bandwidth of your ML engineers to focus on more pressing matters.
Why you need to explain machine learning models
How to Explain Your ML Models?
The Importance of Model Fairness and Interpretability in AI Systems
Explainability and Auditability in ML: Definitions, Techniques, and Tools