📌 Model Interpretability Summary
Model interpretability refers to how easily a human can understand the decisions or predictions made by a machine learning model. It is about making the inner workings of a model transparent, so people can see why it made a certain choice. This is important for trust, accountability, and identifying mistakes or biases in automated systems.
🙋🏻♂️ Explain Model Interpretability Simply
Imagine a teacher marking your exam and explaining the reasons behind each mark. Model interpretability is like asking the model to show its working so you know how it reached its answer. If you can follow the steps, you are more likely to trust the result and spot any errors.
📅 How Can it be used?
Model interpretability can help explain credit approval decisions to customers by showing which factors influenced the outcome.
🗺️ Real World Examples
A hospital uses an AI model to predict which patients are at risk of complications. Doctors rely on model interpretability tools to see which symptoms or test results led to a high-risk prediction, helping them make informed treatment decisions.
A bank uses a machine learning model to assess loan applications. By making the model interpretable, staff can show applicants which financial details impacted the decision, helping ensure fair and transparent lending.
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