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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 51–60 of 2050 papers

TitleStatusHype
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph BenchmarkCode1
Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPsCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Statistical Uncertainty in Word Embeddings: GloVe-VCode1
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of PovertyCode1
Movie Revenue Prediction using Machine Learning ModelsCode1
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationCode1
GeoGalactica: A Scientific Large Language Model in GeoscienceCode1
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation TasksCode1
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