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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 1451–1475 of 2050 papers

TitleStatusHype
A Base Model Selection Methodology for Efficient Fine-Tuning—0
On summarized validation curves and generalization—0
Interpretable Models for Understanding Immersive Simulations—0
The column measure and Gradient-Free Gradient Boosting—0
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis—0
How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions—0
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
Not again! Data Leakage in Digital Pathology—0
Predictive Multiplicity in ClassificationCode0
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization—0
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference ModelsCode0
Deep Elastic Networks with Model Selection for Multi-Task Learning—0
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A SurveyCode0
Quality Estimation for Image Captions Based on Large-scale Human EvaluationsCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
On the overestimation of widely applicable Bayesian information criterion—0
Model Selection With Graphical Neighbour Information—0
EPP: interpretable score of model predictive powerCode0
Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
Minimum Description Length Revisited—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
metric-learn: Metric Learning Algorithms in PythonCode0
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