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Comparison of classifiers

Comparison of classifiers. Usman Roshan CS 675. Comparison of classifiers. Empirical comparison of supervised classifiers – ICML 2006 Do we need hundreds of classifiers – JMLR 2014. Empirical comparison of supervised classifiers – ICML 2006. Classifiers compared SVM ANN Logreg

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Comparison of classifiers

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  1. Comparison of classifiers Usman Roshan CS 675

  2. Comparison of classifiers • Empirical comparison of supervised classifiers – ICML 2006 • Do we need hundreds of classifiers – JMLR 2014

  3. Empirical comparison of supervised classifiers – ICML 2006 • Classifiers compared • SVM • ANN • Logreg • Naïve Bayes • KNN • Random forest • Decision trees • Bagged trees

  4. Empirical comparison of supervised classifiers – ICML 2006 • Performance metrics • Threshold metrics • Accuracy • F-score • Lift • Ordering/rank metrics • Area under curve (AUC) • Average precision • Precision/recall breakpoint • Probability metrics • Root mean square distance (RMSD) • Cross-entropy (MXE)

  5. Empirical comparison of supervised classifiers – ICML 2006 • Datasets

  6. Empirical comparison of supervised classifiers – ICML 2006

  7. Empirical comparison of supervised classifiers – ICML 2006

  8. Empirical comparison of supervised classifiers – ICML 2006

  9. Empirical comparison of supervised classifiers – ICML 2006

  10. Do we need hundreds of classifiers – JMLR 2014 • Datasets • 121 in total from UCI • Classifiers • 179 in total • Implemented in C/C++, Matlab, Weka, and R

  11. Do we need hundreds of classifiers – JMLR 2014 • Classifiers • Discriminant analysis (20) • Bayesian methods (6) • Neural networks (21) • SVMs (10) • Decision trees (14) • Rule-based (12) • Boosting (20) • Bagging (24) • Stacking (2) • Random forests (8) • Other ensembles (11) • Generalized linear models (5) • Nearest neighbor (5) • Partial least squares and PCA regression (6) • Logistic and multinomial regression (3) • Multivariate adaptive splines (2) • Other methods (10)

  12. Do we need hundreds of classifiers – JMLR 2014

  13. Do we need hundreds of classifiers – JMLR 2014

  14. Do we need hundreds of classifiers – JMLR 2014

  15. Do we need hundreds of classifiers – JMLR 2014

  16. Do we need hundreds of classifiers – JMLR 2014

  17. Do we need hundreds of classifiers – JMLR 2014 Binary classification only

  18. Do we need hundreds of classifiers – JMLR 2014 Binary classification only

  19. Limitations of study • Not all classifiers were tuned by cross-validation • Comparison on big datasets – deep learning • No runtimes provided

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