ML Interpretability
ML Interpretability is the ability to understand how a machine learning model makes predictions and which factors influence its decisions. It helps make model behavior more transparent and easier for people to analyze.
What is ML Interpretability?
ML Interpretability involves examining how a model uses input features, learned patterns, and other factors to produce its outputs. Depending on the model and method, interpretability can include feature importance, decision rules, prediction explanations, or techniques such as LIME and SHAP.
Why is ML Interpretability Important?
Interpretability helps teams understand whether models are making reasonable decisions and identify potential errors, biases, or unexpected behavior. It can also support accountability, debugging, regulatory requirements, and greater confidence when deploying machine learning systems.
Common use cases
ML Interpretability is commonly used in healthcare, finance, fraud detection, risk assessment, compliance, model debugging, and other applications where understanding model decisions is important.