In DarioS/ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

When a choice about a model to use is made and specified to crossValidate, performance tuning of a range of typical values may be automatically made or manually specified. Below is a table of models and their default tuning values, which can be activated by specifying the extraParams parameter's list element "select", "train" or "predict"'s element's "tuneParams" element as "auto". Otherwise, it is a named list of parameter values.

Model | Keyword | Default Parameters | Other Parameters ----|:----:|:----:|:----: Random Forest | "randomForest" | mTryProportion = c(0.10, 0.25, 0.33, 0.5) and num.trees = c(1, 10, 100) | See ?ranger::ranger Random Survival Forest | "randomSurvivalForest" | mTryProportion = c(0.10, 0.25, 0.33, 0.5) and num.trees = c(1, 10, 100) | See ?randomForestSRC::rfsrc Extreme Gradient Boosting | "XGB" | mTryProportion = c(0.10, 0.25, 0.33, 0.5) and nrounds = c(5, 10) | See ?xgboost::xgboost Support Vector Machine | "SVM" | kernel = c("linear", "polynomial", "radial", "sigmoid") and cost = 10^(-3:3)) | See ?e1071::svm naive Bayes Kernel | "naiveBayes" | difference = c("unweighted", "weighted") | weighting = c("height difference", "crossover distance") and minDifference, a number 0 or higher. Mixtures of Normals | "mixturesNormals" | nbCluster = 1:2 | difference = c("unweighted", "weighted") and weighting = c("height difference", "crossover distance") and minDifference, a number 0 or higher.

Note that the last two models are not wrappers but custom implementations in this package. The weighting parameter specifies how the differences between classes are calculated. Either the height difference of a class's fitted density to another class's nearest density is used or the horizontal distance from the measurement value to the nearest position where the density of that class crosses any other class. The distances are only used if difference is weighted. minDifference specifies the minimum distance between densities for a feature to have a vote for a particular class. The default of 0 means that every feature votes for the class which has the highest density at that point, regardless how close the density of any other class is to it.

DarioS/ClassifyR documentation built on Dec. 19, 2024, 8:22 p.m.