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(PDF) Feature ranking using linear svm | Chih-Jen Lin –
In line with Bayesian Networks graphical semantics Tsamardinos et al. When selecting the best results in nested subsets of features, Yin-Wen Chang obtained significantly better results than anyone else with her SVM feature ranking. Guyon, J. Despite the different views, the motivation is the same. In an alternative case, if the customer decided to decline the proposed contract, the Company would be interested to know decision if the price will be slightly lower. The nodes in the graph are identified with random variables, and joint probability distributions are defined by taking products over functions defined on connected subsets of nodes. Classical randomized designs Rubin, stand out as especially appealing assignment mechanisms designed to make inference for causal effects.
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Definition of a new Fscore. Brown and Tsamardinos, with only 9 features. If the causal paths to the target are not interrupted by the manipulations, adding some predictive non-MB variables like the light green nodes in Figure 1 may help improving perfor- mances when a biased classifier is used, e.