Edge 253: Interpretability Methods: Partial Dependence Plots
Was this email forwarded to you? Sign up here Edge 253: Interpretability Methods: Partial Dependence PlotsPartial dependence plots, interpretable time series forecasting and Google's fairness indicators.In this issue:
💡 ML Concept of the Day: Global Model-Agnostic Interpretability Methods: Partial Dependence PlotsAs part of our series about ML interpretability, we recently cover global model-agnostic methods that try to infer a global explanation for the behavior of a “black box” model. Partial dependent plots(PDP) is one of the best-established global interpretability methods in ML. Conceptually, PDP illustrate how each predictor variable affects the model predictions. More specifically, PDP shows the dependencies between a target response and a set of relevant model features. In order to achieve this, PDP uses a regression function that quantifies the marginal impacts of each feature in the output prediction... 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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