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Using New Models to Analyze Complex Regularities of the World: Commentary on Musso et al. (2013)
Petri Nokelainen,Tomi Silander +1 more
- Vol. 2, Iss: 1, pp 78-82
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TLDR
This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling.Abstract:
This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is a risk that we model perfect nonsense perfectly. Our second concern is related to the relevance of comparing multilayer perceptron neural networks and linear discriminant analysis classification accuracy indices. We find this problematic, as it is like comparing apples and oranges. It would have been easier to interpret the model and the variable (group) importance’s if the authors would have compared MLP to some discriminative classifier, such as group lasso logistic regression. Finally, we conclude our commentary with a discussion about the predictive properties of the adopted data modeling approach.read more
Citations
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The role of natural abilities, intrinsic characteristics, and extrinsic conditions in air traffic controllers’ vocational development
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References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
TL;DR: Algorithmic models have been widely used in fields outside statistics as discussed by the authors, both in theory and practice, and can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets.
Journal Article
Statistical modeling: The two cultures
TL;DR: If the goal as a field is to use data to solve problems, then the statistical community needs to move away from exclusive dependence on data models and adopt a more diverse set of tools.
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The group lasso for logistic regression
TL;DR: An efficient algorithm is presented, that is especially suitable for high dimensional problems, which can also be applied to generalized linear models to solve the corresponding convex optimization problem.