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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 291–300 of 2050 papers

TitleStatusHype
A Junction Tree Framework for Undirected Graphical Model Selection—0
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization—0
Active Comparison of Prediction Models—0
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter—0
Black-box Selective Inference via Bootstrapping—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
Action-State Dependent Dynamic Model Selection—0
On The Stability of Interpretable Models—0
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions—0
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models—0
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