Model selection with the Loss Rank Principle
A key issue in statistics and machine learning is to automatically select the "right" model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (k NN) regression or the polynomial degree in regression with polynomials. We suggest a novel principle-the Loss Rank Principle (LoRP)-for model selection in regression and classification. It is based on the loss rank, which counts how many other (fictitious) data would be fitted better. LoRP selects the model that has...[Show more]
|Collections||ANU Research Publications|
|Source:||Computational Statistics and Data Analysis|
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