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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 2031–2040 of 2050 papers

TitleStatusHype
Stability selection enables robust learning of partial differential equations from limited noisy dataCode0
Risk Controlled Image RetrievalCode0
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and CostCode0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer VisionCode0
Machine learning for sports betting: should model selection be based on accuracy or calibration?Code0
Batch Value-function Approximation with Only RealizabilityCode0
Machine learning in policy evaluation: new tools for causal inferenceCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
mage based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimizationCode0
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