Edge 255: Interpretability Methods: Accumulated Local Effects (ALE)
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💡 ML Concept of the Day: Global Model-Agnostic Interpretability Methods: Accumulated Local Effects (ALE)In the previous issue of this series about ML interpretability, we explored the concept of partial dependence plots(PDP). Today, we would like to discuss an alternative to PDP known as accumulated local effects(ALE). Just like PDP, ALE focuses on explaining the average impact of features on the model prediction. The main difference is that ALE mitigates some of the bias and performance challenges of PDP. The main limitation of PDP arises when there is a significant correlation between the features in an ML model. In those cases, PDP outputs are highly unreliable. This is mostly due to the fact that PDP outputs are based on the averages of the predictions which obscure dependencies between the features. ALE also uses a conditional feature distribution as an input but focuses on differences in predictions instead of the average. Following our previous examples of a model that predicts the price of a house based on its distance from the city center and size of the building area, ALE will take the differences of the predictions of houses with around 3000sqt to those with 3500 sqt. The result provides a clear perspective of the impact of the size of the living area without the effect of the correlated features... Subscribe to TheSequence to read the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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Edge 255: Interpretability Methods: Accumulated Local Effects (ALE)
Tuesday, December 27, 2022
ALE method, OpenAI Microscope and IBM's AI 360 Explainability Toolkit.
OpenAI Gets Into the Text-to-3D Game with Point-E
Sunday, December 25, 2022
Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.
Edge 254: InstructGPT is the Model that Inspired the Famous ChatGPT
Thursday, December 22, 2022
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Edge 253: Interpretability Methods: Partial Dependence Plots
Tuesday, December 20, 2022
Partial dependence plots, interpretable time series forecasting and Google's fairness indicators.
Security: The Most Ignored Area of MLOps
Sunday, December 18, 2022
Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.
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