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Interpretable Machine Learning: Difference between revisions
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Interpretable Machine Learning (IML) is a subfield of [[Machine Learning|machine learning]] that focuses on creating models that provide understandable and interpretable results. Unlike black-box models that provide no insight into their internal workings, interpretable models allow users to understand, trust, and potentially manipulate the model's decision-making process. | Interpretable Machine Learning (IML) is a subfield of [[Machine Learning|machine learning]] that focuses on creating models that provide understandable and interpretable results. Unlike black-box models that provide no insight into their internal workings, interpretable models allow users to understand, trust, and potentially manipulate the model's decision-making process. | ||
[[Image:Detail-147687.jpg|thumb|center|A computer screen displaying a machine learning model with clear, interpretable results.]] | [[Image:Detail-147687.jpg|thumb|center|A computer screen displaying a machine learning model with clear, interpretable results.|class=only_on_mobile]] | ||
[[Image:Detail-147688.jpg|thumb|center|A computer screen displaying a machine learning model with clear, interpretable results.|class=only_on_desktop]] | |||
== Importance of Interpretability == | == Importance of Interpretability == | ||