版本变化2.6.0 o两级easy-hard分类器补充道。版本变化2.2.0 o getclass PredictParams不再是一个插槽。每个预测函数需要返回向量类的一个因素,一个数值向量类分数第二课堂,与一列或一个数据帧的预测类和另一个二等的分数。o交叉验证使用折叠确保样本属于每个类都在大约相同的比例对整个数据集o分类可以通过使用previousTrained重用以前的拟合模型分类功能。o使用基因集特征选择和网络。分类可以使用meta-features源自个体特性用于特征选择。o tTestSelection函数特征选择基于普通t检验统计排名。现在默认特征选择函数,如果没有指定。o调优参数优化指标是通过提供一个指定TrainParams tuneOptimise参数而不是根据ResubstituteParams使用特征选择。o广泛支持DataFrame 2.0.0版本)的变化和MultiAssayExperiment数据集的特征选择和分类功能。 o The majority of processing is now done in the DataFrame method for functions that implement methods for multiple kinds of inputs. o Elastic net GLM classifier and multinomial logistic regression classifier wrapper functions. o Plotting functions have a new default style using a white background with black axes. o Vignette simplified and uses a new mass cytometry data set with clearer differences between classes to demonstrate classification and its performance evaluation. Changes in version 1.12.0 o Alterations to make plots compatible with ggplot versions 2.2 and greater. o calcPerformance can calculate some performance metrics for classification tasks based on data sets with more than two classes. o Sample-wise metrics, like sample-specific error rate and sample-specific accuracy are calculated by calcPerformance and added to the ClassifyResult object, rather than by samplesMetricMap and being inaccessible to the end-user. Changes in version 1.10.0 o errorMap replaced by samplesMetricMap. The plot can now show either error rate or accuracy. Changes in version 1.8.0 o Ordinary k-fold cross-validation option added. o Absolute difference of group medians feature selection function added. Changes in version 1.4.0 o Weighted voting mode that uses the distance from an observation to the nearest crossover point of the class densities added. o Bartlett Test selection function included. o New class SelectResult. rankPlot and selectionPlot can additionally work with lists of SelectResult objects. All feature selection functions now return a SelectResult object or a list of them. o priorSelection is a new selection function for using features selected in a prior cross validation for a new data set classification. o New weighted voting mode, where the weight is the distance of the x value from the nearest crossover point of the two densities. Useful for predictions with skewed features. Changes in version 1.2.0 o More classification flexibility, now with parameter tuning integrated into the process. o New performance evaluation functions, such as a ROC curve and a performance plot. o Some existing predictor functions are able to return class scores, not just class labels. Changes in version 1.0.0 o First release of the package, which allows parallelised and customised classification, with many convenient performance evaluation functions.