fs.absT(MLInterfaces)
fs.absT()所属R语言包：MLInterfaces
support for feature selection in cross-validation
在交叉验证特征选择的支持
译者：生物统计家园网 机器人LoveR
描述----------Description----------
support for feature selection in cross-validation
在交叉验证特征选择的支持
用法----------Usage----------
fs.absT(N)
fs.probT(p)
fs.topVariance(p)
参数----------Arguments----------
参数：N
number of features to retain; features are ordered by descending value of abs(two-sample t stat.), and the top N are used.
多项功能保留功能ABS值下降（两样本t统计）。排序，前N。
参数：p
cumulative probability (in (0,1)) in the distribution of absolute t statistics above which we retain features
绝对高于我们保留功能的t统计量的分布累积概率（（0,1））
Details
详情----------Details----------
This function returns a function that will be used as a parameter to xvalSpec in applications of MLearn.
这个函数返回一个将作为一个参数xvalSpec应用MLearn使用的功能。
值----------Value----------
a function is returned, that will itself return a formula consisting of the selected features for application of MLearn.
返回一个函数，将返回一个公式应用MLearn的所选功能。
注意----------Note----------
The functions fs.absT and fs.probT are two examples of approaches to embedded feature selection that make sense for two-sample prediction problems. For selection based on linear models or other discrimination measures, you will need to create your own selection helper, following the code in these functions as examples.
职能fs.absT和fs.probT嵌入式功能选择，使两样本预测问题的有意义的方法的两个例子。基于线性模型或其他歧视措施的选择，您将需要创建您自己选择的帮手，这些功能作为例子的代码。
fs.topVariance performs non-specific feature selection based on the variance. Argument p is the variance percentile beneath which features are discarded.
fs.topVariance执行基于方差的非特定功能的选择。参数p是方差百分下方的功能将被丢弃。
作者（S）----------Author(s)----------
VJ Carey <stvjc@channing.harvard.edu>
参见----------See Also----------
MLearn
MLearn
举例----------Examples----------
# we will demonstrate this procedure with the crabs data.[与螃蟹数据，我们将演示这一过程。]
# first, create the closure to pick 3 features[首先，创建封闭挑选3功能]
demFS = fs.absT(3)
# run it on the entire dataset with features excluding sex[整个数据集上运行，它不包括性功能]
demFS(sp~.-sex, crabs)
# emulate cross-validation by excluding last 50 records[仿效交叉验证，不包括过去50个记录]
demFS(sp~.-sex, crabs[1:150,])
# emulate cross-validation by excluding first 50 records -- different features retained[不包括第50条记录模拟交叉验证 - 保留不同的特点]
demFS(sp~.-sex, crabs[51:200,])
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 01:04:02
