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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 261–270 of 2050 papers

TitleStatusHype
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework—0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization—0
Reassessing Large Language Model Boolean Query Generation for Systematic Reviews—0
Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection—0
A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model—0
High-Dimensional Importance-Weighted Information Criteria: Theory and Optimality—0
Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal—0
Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class PredictionCode0
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
Machine Learning: a Lecture Note—0
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