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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 1501–1510 of 2050 papers

TitleStatusHype
Stability selection enables robust learning of partial differential equations from limited noisy dataCode0
Improving classification performance by feature space transformations and model selection—0
Sparsely Activated NetworksCode0
On the Evaluation of Conditional GANsCode0
Change point detection for graphical models in the presence of missing valuesCode0
PreCall: A Visual Interface for Threshold Optimization in ML Model Selection—0
Competing Models—0
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction—0
Multiple Testing and Variable Selection along the path of the Least Angle RegressionCode0
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data—0
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