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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 211–220 of 2050 papers

TitleStatusHype
Statistical inference for quantum singular models—0
An AutoML-based approach for Network Intrusion Detection—0
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions—0
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices—0
LLM4DS: Evaluating Large Language Models for Data Science Code Generation—0
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery—0
Continuous Bayesian Model Selection for Multivariate Causal Discovery—0
A survey of probabilistic generative frameworks for molecular simulationsCode0
Evaluating Gender Bias in Large Language Models—0
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
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