Interpretable Machine Learning: Techniques for Understanding and Explaining Black-Box Models
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Abstract
Interpretable machine learning has become increasingly important as complex black-box models are being deployed in various domains, from finance to healthcare and beyond. techniques for understanding and explaining black-box models, aiming to shed light on their decision-making process and improve trust and accountability. Interpretable machine learning techniques range from simple model-agnostic methods to more sophisticated model-specific approaches. Model-agnostic techniques, such as feature importance scores and partial dependence plots, provide insights into the overall behaviour of black-box models without relying on internal model structures. On the other hand, model-specific techniques, such as decision trees and rule-based models, offer interpretable alternatives to black-box models by explicitly representing the decision-making process in a human-readable format.
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