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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 901–910 of 2050 papers

TitleStatusHype
Understanding the double descent curve in Machine Learning—0
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks—0
Execution-based Evaluation for Data Science Code Generation ModelsCode0
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves—0
MGTCOM: Community Detection in Multimodal GraphsCode0
Robust Model Selection of Gaussian Graphical Models—0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
Beyond Conjugacy for Chain Event Graph Model Selection—0
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?—0
Oracle Inequalities for Model Selection in Offline Reinforcement Learning—0
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