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Posts in the series: Benefits of Decision Modeling What is a decision model and what is DMN Decision Modeling: Decision Requirements Decision Modeling: Decision Logic Decision Modeling: Finding and modeling decisions [this post] Decision Modeling: Input Data and Knowledge Sources Building decision tables from decision models ML, AI and other forms of Data Analysis and Advanced Analytics -- One of the most important things in decision modeling is identifying decisions. This move away from trying to capture individual rules – in a sheet or list – and toward modeling meaningful decisions is crucial to successfully applying decision modeling
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In Shumailov et al. (2023)'s paper , The Curse of Recursion: Training on Generated Data Makes Models Forget, the concept of Model Collapse is explored. The TLDR concept being, when we extrapolate a model from data, we often lose the extremes or variance in the data. Over time, if we then use data generated from our models, we will lose this variation entirely and converge down to a single point. Does this mean one day our foundation models will respond to all questions with a single word if we are not careful?
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In comparison to the other approaches, this procedure is highly rapid and exact because the labels are supplied before creating the model. Pachinko Allocation Model (PAM): The Pachinko Allocation Model (PAM) is a more advanced version of the Latent Dirichlet Allocation Model
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