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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 401–425 of 2050 papers

TitleStatusHype
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI—0
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion—0
A simple application of FIC to model selection—0
CLAMS: A System for Zero-Shot Model Selection for Clustering—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels—0
Classification with Scattering Operators—0
Classification with Sparse Overlapping Groups—0
client2vec: Towards Systematic Baselines for Banking Applications—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
Clipper: A Low-Latency Online Prediction Serving System—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
Closing the gap between open-source and commercial large language models for medical evidence summarization—0
A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection—0
biastest: Testing parameter equality across different models in Stata—0
Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III—0
Clustering evolving data using kernel-based methods—0
A Statistical Framework for Model Selection in LSTM Networks—0
Clustering - What Both Theoreticians and Practitioners are Doing Wrong—0
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Consensual Aggregation on Random Projected High-dimensional Features for Regression—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Consistencies and inconsistencies between model selection and link prediction in networks—0
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