ML interpretability refers to the ability to explain or present in understandable human terms the decisions, behaviors, or predictions made by machine learning (ML) models. It is an essential aspect of machine learning, which helps to build trust, facilitate transparency, and enable actionable insights into machine learning models.
ML Interpretability in practice
ML interpretability works by employing various techniques and approaches, both model-specific and model-agnostic, to provide insights into how a machine learning model arrived at its conclusions or decisions.
Model-specific interpretability, as the name implies, is designed for a particular type of model. For instance, decision trees and linear regression models are inherently interpretable, as their decisions can be followed along branches or viewed in terms of weights for each feature.
Model-agnostic interpretability, on the other hand, is designed to work with any machine learning model. These methods include partial dependence plots, Shapley additive explanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME), which create simpler, interpretable models that approximate the behavior of the original model around a particular input or set of inputs.
The key to ML interpretability is to strike a balance between accuracy and interpretability. More complex models like neural networks may provide higher accuracy but are often considered "black boxes" due to their lack of interpretability. Conversely, simpler models may provide lower accuracy but are easier to interpret and understand. Thus, the choice of model and interpretability method depends on the specific requirements and constraints of the task at hand.
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