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Model Selection and Assessment Using Cross-indexing

Model Selection and Assessment Using Cross-indexing. Juha Reunanen ABB, Web Imaging Systems, Finland. Model Selection Using Cross-Validation. Choose a search algorithm – for example: hill-climbing, grid search, genetic algorithm Evaluate the models using cross-validation

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Model Selection and Assessment Using Cross-indexing

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  1. Model Selection and Assessment Using Cross-indexing Juha Reunanen ABB, Web Imaging Systems, Finland

  2. Model Selection Using Cross-Validation • Choose a search algorithm – for example: hill-climbing, grid search, genetic algorithm • Evaluate the models using cross-validation • Select the model that gives the best CV score

  3. Multiple-Comparison Procedure (D. D. Jensen and P. R. Cohen: Multiple Comparisons in Induction Algorithms, Machine Learning, volume 38, pages 309–338, 2000) • Example: Choosing an investment advisor • Criterion: Predict stock market change (+/–) correctly for 11 out of 14 days • You evaluate 10 candidates • Your friend evaluates 30 candidates • If everyone is just guessing, your probability of accepting is 0.253, your friend’s 0.583

  4. The Problem • Overfitting on the first level of inference:Increasing model complexity may decrease the training error while the test error goes up • Overfitting on the second level of inference:Making the search more intense may decrease the CV error estimate, even if the test error would actually go up

  5. Overfitting Visualized Model Complexity, or Number of Models Evaluated

  6. Solutions • First level of inference: • Regularization – penalize complex models • Model selection – welcome to the second level... • Second level of inference: • Regularization! (G. C. Cawley and N. L. C. Talbot: Preventing over-fitting during model selection via Bayesian regularisation of the hyper-parameters, Journal of Machine Learning Research, volume 8, pages 841-861, 2007) • Another layer of (cross-)validation...

  7. Another Layer of Validation • A lot of variance: the estimate related to the winner gets biased (in the MCP sense) • Cross-validation makes it smoother, but does not remove the problem

  8. The Cross-indexing Trick • Assume an outer loop of cross-validation using five folds • Use (for example) three folds to determine the best depth, and the rest two to assess it • This essentially removes the multiple-comparison effect • Revolve, and average (or, create an ensemble) • Previously shown to work in feature selection (Juha Reunanen: Less Biased Measurement of Feature Selection Benefits, SLSFS 2005, LNCS 3940, pages 198–208, 2006)

  9. Competition Entries • Stochastic search guided by cross-validation • Several candidate models (and corresponding search processes running pseudo-parallel):Prepro+naiveBayes, PCA+kernelRidge, GS+kernelRidge, Prepro+linearSVC, Prepro+nonlinearSVC, Relief+neuralNet, RF, and Boosting (with neuralNet, SVC and kernelRidge) • Final selection and assessment using the cross-indexing criterion

  10. Milestone Results Agnostic learning ranks as of December 1st, 2006 Yellow: CLOP model. CLOP prize winner: Juha Reunanen (both ave. rank and ave. BER). Best ave. BER held by Reference (Gavin Cawley) with “the bad”.

  11. Models Selected

  12. Conclusions • Because of multiple-comparison procedures (MCPs) on the different levels of inference, validation is often used to estimate final performance • On the second level, the cross-indexing trick may give estimates that are less biased (when comparing to straightforward outer-loop CV)

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