classifierOutput-class(MLInterfaces)
classifierOutput-class()所属R语言包：MLInterfaces
Class "classifierOutput"
类“classifierOutput”
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This class summarizes the output values from different classifiers.
这个类的总结，从不同的分类器的输出值。
类的对象----------Objects from the Class----------
Objects are typically created during the application of a supervised machine learning algorithm to data and are the value returned. It is very unlikely that any user would create such an object by hand.
对象通常是建立在有监督的机器学习算法，对数据的应用和正在返回的值。这是不太可能的，任何用户手工创建这样一个对象。
插槽----------Slots----------
testOutcomes: Object of class "factor" that
testOutcomes：Object类的"factor"，
testPredictions: Object of class "factor" that
testPredictions：Object类的"factor"，
testScores: Object of class "ANY" – this element will include matrices or vectors or arrays that include information that is typically related to the posterior probability of occupancy of the predicted class or of all classes. The actual contents of this slot can be determined by inspecting the converter
testScores：Object类的"ANY" - 这个元素将包括矩阵或向量或阵列，包括信息，通常与预测类或占用各阶层的后验概率。此插槽的实际内容，可以决定通过检查转换器
trainOutcomes: Object of class "factor" that
trainOutcomes：Object类的"factor"，
trainPredictions: Object of class "factor" that
trainPredictions：Object类的"factor"，
trainScores: Object of class "ANY" see the description of testScores above; the same information is returned, but applicable to the training set records.
trainScores：Object类的"ANY"testScores上面的描述;返回相同的信息，但适用于训练集的记录。
trainInd: Object of class "numeric" with of
类trainInd与"numeric"：对象
RObject: Object of class "ANY" – when the trainInd parameter of the MLearn call is numeric, this slot holds the return value of the underlying R function that carried out the predictive modeling. For example, if rpartI was used as MLearn method, Robject holds an instance of the rpart S3 class, and plot and text methods can be applied to this. When the trainInd parameter of the MLearn call is an instance of xvalSpec, this slot holds a list of results of cross-validatory iterations. Each element of this list has two elements: test.idx, giving the numeric indices of the test cases for the associated cross-validation iteration, and mlans, which is the classifierOutput for the associated iteration. See the example for an illustration of 'digging out' the predicted probabilities associated with each
RObject：Object类的"ANY" - trainIndMLearn调用参数是数字时，这个槽持有底层的R函数的返回值进行预测模型。例如，如果rpartI被用作MLearnmethod，Robject拥有rpart S3类的一个实例，plot和text方法可以应用于此。当trainInd呼叫参数MLearn是实例xvalSpec，这个槽持有的跨validatory迭代的结果list。这个名单中的每个元素有两个要素：test.idx，给予相关的交叉验证迭代的测试用例的数字指标，mlans，classifierOutput相关迭代 。看到的例子，说明“挖掘出”相互关联的概率预测
embeddedCV: logical value that is TRUE if
embeddedCV：逻辑值TRUE，如果
fsHistory: list of features selected
fsHistory：选择功能列表
learnerSchema: propagation of the learner
learnerSchema：学习者的传播
call: Object of class "call" – records the
call：Object类的"call" - 记录
方法----------Methods----------
confuMat signature(obj = "classifierOutput"): Compute
confuMatsignature(obj = "classifierOutput")：计算
confuMatTrain signature(obj = "classifierOutput"): Compute the confusion matrix for training set. Typically yields optimistically biased
confuMatTrainsignature(obj = "classifierOutput")：为训练集计算混淆矩阵。通常情况下产生乐观的偏见
RObject signature(obj = "classifierOutput"): The R object returned by the underlying classifier. This can then be passed on to
RObjectsignature(obj = "classifierOutput")：R对象的基本分类返回。这可以被传递给
trainInd signature(obj = "classifierOutput"): Returns
trainIndsignature(obj = "classifierOutput")：返回
show signature(object = "classifierOutput"): A print method
显示signature(object = "classifierOutput")：打印方法
predictions signature(object = "classifierOutput"): Print the predicted classes for each sample/individual. The predictions
预测signature(object = "classifierOutput")：打印每个样品/个人预测类。预测
predictions signature(object = "classifierOutput", t = "numeric"): Print the predicted classes for each sample/individual that have a testScore greater or equal than t. The predictions for the training set are the training outcomes. Non-predicted cases and cases that
预测signature(object = "classifierOutput", t = "numeric")：每个样品/个人打印的预测类，有一个testScore大于或等于比t。训练集的预测是训练的成果。非预测的情况和情况
predScores signature(object = "classifierOutput"): Returns the prediction scores for each sample/individual. The
predScoressignature(object = "classifierOutput")：返回每个样品/个人的预测分数。 “
testScores signature(object = "classifierOutput"): ...
testScoressignature(object = "classifierOutput")...
testPredictions signature(object = "classifierOutput"): Print
testPredictionssignature(object = "classifierOutput")：打印
testPredictions signature(object = "classifierOutput", t = "numeric"): Print the predicted classes for each sample/individual in the test set that have a testScore greater or equal than t. Non-predicted cases and cases that
testPredictionssignature(object = "classifierOutput", t = "numeric")：每个样品/测试集的个人打印的预测类testScore大于或等于比t。非预测的情况和情况
trainScores signature(object = "classifierOutput"): ...
trainScoressignature(object = "classifierOutput")...
trainPredictions signature(object = "classifierOutput"): Print the predicted classes for each
trainPredictionssignature(object = "classifierOutput")：打印每个预测类
trainPredictions signature(object = "classifierOutput", t = "numeric"): Print the predicted classes for each sample/individual in the train set that have a testScore greater or equal than t. Non-predicted cases and cases that
trainPredictionssignature(object = "classifierOutput", t = "numeric")：每个样品/个人在列车集打印，预测类，有testScore大于或等于比t。非预测的情况和情况
fsHistory signature(object = "classifierOutput"): ...
fsHistorysignature(object = "classifierOutput")...
作者（S）----------Author(s)----------
V. Carey
举例----------Examples----------
showClass("classifierOutput")
library(golubEsets)
data(Golub_Train) # now cross-validate a neural net[现在交叉验证神经网络]
set.seed(1234)
xv5 = xvalSpec("LOG", 5, balKfold.xvspec(5))
m2 = MLearn(ALL.AML~., Golub_Train[1000:1050,], nnetI, xv5,
size=5, decay=.01, maxit=1900 )
testScores(RObject(m2)[[1]]$mlans)
alls = lapply(RObject(m2), function(x) testScores(x$mlans))
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-26 01:03:44
