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  "Title": "Nested Loop Cross Validation",
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  "Description": "Nested loop cross validation for classification purposes\nfor misclassification error rate estimation. The package\nsupports several methodologies for feature selection: random\nforest, Student t-test, limma, and provides an interface to the\nfollowing classification methods in the 'MLInterfaces' package:\nlinear, quadratic discriminant analyses, random forest,\nbagging, prediction analysis for microarray, generalized linear\nmodel, support vector machine (svm and ksvm). Visualizations to\nassess the quality of the classifier are included: plot of the\nranks of the features, scores plot for a specific\nclassification algorithm and number of features,\nmisclassification rate for the different number of features and\nclassification algorithms tested and ROC plot. For further\ndetails about the methodology, please check: Markus Ruschhaupt,\nWolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004)\n<doi:10.2202/1544-6115.1078>.",
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    {
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      "title": "Nested Loop Cross-Validation",
      "topics": [
        "nlcv"
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