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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 791–800 of 2050 papers

TitleStatusHype
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
Beyond Conjugacy for Chain Event Graph Model Selection—0
scikit-fda: A Python Package for Functional Data AnalysisCode2
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?—0
Data Models for Dataset Drift Controls in Machine Learning With Optical ImagesCode1
Toward Unsupervised Outlier Model SelectionCode1
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
Oracle Inequalities for Model Selection in Offline Reinforcement Learning—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
Differentiable Model Selection for Ensemble LearningCode0
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