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  "Title": "Ordinal Forests: Prediction and Variable Ranking with Ordinal\nTarget Variables",
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  "Description": "The ordinal forest (OF) method allows ordinal regression\nwith high-dimensional and low-dimensional data. After having\nconstructed an OF prediction rule using a training dataset, it\ncan be used to predict the values of the ordinal target\nvariable for new observations. Moreover, by means of the\n(permutation-based) variable importance measure of OF, it is\nalso possible to rank the covariates with respect to their\nimportance in the prediction of the values of the ordinal\ntarget variable. OF is presented in Hornung (2020). NOTE:\nStarting with package version 2.4, it is also possible to\nobtain class probability predictions in addition to the class\npoint predictions. Moreover, the variable importance values can\nalso be based on the class probability predictions. Preliminary\nresults indicate that this might lead to a better\ndiscrimination between influential and non-influential\ncovariates. The main functions of the package are: ordfor()\n(construction of OF) and predict.ordfor() (prediction of the\ntarget variable values of new observations). References:\nHornung R. (2020) Ordinal Forests. Journal of Classification\n37, 4–17. <doi:10.1007/s00357-018-9302-x>.",
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