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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 1691–1700 of 2050 papers

TitleStatusHype
Towards a more efficient representation of imputation operators in TPOT—0
Context tree selection for functional dataCode0
Comparing Bayesian Models of Annotation—0
Learning Sparse Neural Networks through L_0 Regularization—0
Parameter-free online learning via model selection—0
Debiased Machine Learning of Set-Identified Linear Models—0
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence—0
The information bottleneck and geometric clusteringCode0
Model-Based Clustering of Time-Evolving Networks through Temporal Exponential-Family Random Graph Models—0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
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