Weka_classifier_rules(RWeka)
Weka_classifier_rules()所属R语言包：RWeka
R/Weka Rule Learners
/ Weka中第学习者的
译者：生物统计家园网 机器人LoveR
描述----------Description----------
R interfaces to Weka rule learners.
R接口Weka中规则的学习者。
用法----------Usage----------
JRip(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
M5Rules(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
OneR(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
PART(formula, data, subset, na.action,
control = Weka_control(), options = NULL)
参数----------Arguments----------
参数：formula
a symbolic description of the model to be fit.
一个象征性的模型来描述是合适的。
参数：data
an optional data frame containing the variables in the model.
一个可选的数据框包含在模型中的变量。
参数：subset
an optional vector specifying a subset of observations to be used in the fitting process.
一个可选的矢量指定的装配过程中可以使用的观测值的一个子集。
参数：na.action
a function which indicates what should happen when the data contain NAs.
一个函数，它表示当数据包含NA的，应该发生什么。
参数：control
an object of class Weka_control giving options to be passed to the Weka learner. Available options can be obtained on-line using the Weka Option Wizard WOW, or the Weka documentation.
对象的类Weka_control提供选项将被传递给Weka中学习。可用的选项可以使用在WEKA选项向导WOW，或在WEKA文档上线。
参数：options
a named list of further options, or NULL (default). See Details.
一个名为进一步的选项列表，或NULL（默认）。查看详细信息。
Details
详细信息----------Details----------
There are a predict method for predicting from the fitted models, and a summary method based on evaluate_Weka_classifier.
有一个predict从拟合模型的预测方法，并summary方法的基础上evaluate_Weka_classifier。
JRip implements a propositional rule learner, “Repeated Incremental Pruning to Produce Error Reduction” (RIPPER), as proposed by Cohen (1995).
JRip实现了命题规则学习，增量修剪，以减少产生错误“（开膛手），科恩（1995）提出的”重复。
M5Rules generates a decision list for regression problems using separate-and-conquer. In each iteration it builds an model tree using M5 and makes the “best” leaf into a rule. See Hall, Holmes and Frank (1999) for more information.
M5Rules生成列表使用独立而治之的回归问题的决定。在每一次迭代中，它建立了一个模型树中使用M5和“最好”的叶成规则。见郝大维，福尔摩斯和Frank（1999）的更多信息。
OneR builds a simple 1-R classifier, see Holte (1993).
OneR建立了一个简单的1-R分类，请参阅霍尔特（1993年）。
PART generates PART decision lists using the approach of Frank and Witten (1998).
PART产生部分决定列表，使用方法弗兰克和威滕（1998）。
The model formulae should only use the + and - operators to indicate the variables to be included or not used, respectively.
模型公式应该只使用+和-运营商表示的变量或不使用，分别。
Argument options allows further customization. Currently, options model and instances (or partial matches for these) are used: if set to TRUE, the model frame or the corresponding Weka instances, respectively, are included in the fitted model object, possibly speeding up subsequent computations on the object. By default, neither is included.
参数options可以进一步定制。目前，选项model和instances（或部分匹配的这些）：如果设置为TRUE，模型的框架或相应的的WEKA情况下，分别拟合模型对象，可能加快后续计算的对象。默认情况下，也不是。
值----------Value----------
A list inheriting from classes Weka_rules and Weka_classifiers with components including
继承类Weka_rules和Weka_classifiers组件，包括列表
参数：classifier
a reference (of class jobjRef) to a Java object obtained by applying the Weka buildClassifier method to build the specified model using the given control options.
参考（类jobjRef），通过应用在WEKA buildClassifier方法来建立模型使用给定的控制选项指定的Java对象。
参数：predictions
a numeric vector or factor with the model predictions for the training instances (the results of calling the Weka classifyInstance method for the built classifier and each instance).
一个数值向量或因子与培训实例（在WEKA classifyInstance内置分类方法，每个实例调用的结果）的模型预测。
参数：call
the matched call.
匹配的呼叫。
参考文献----------References----------
Fast effective rule induction. In A. Prieditis and S. Russell (eds.), Proceedings of the 12th International Conference on Machine Learning, pages 115–123. Morgan Kaufmann. ISBN 1-55860-377-8. http://citeseer.ist.psu.edu/cohen95fast.html
Generating accurate rule sets without global optimization. In J. Shavlik (ed.), Machine Learning: Proceedings of the Fifteenth International Conference. Morgan Kaufmann Publishers: San Francisco, CA. http://www.cs.waikato.ac.nz/~eibe/pubs/ML98-57.ps.gz
Generating rule sets from model trees. Proceedings of the Twelfth Australian Joint Conference on Artificial Intelligence, Sydney, Australia, pages 1–12. Springer-Verlag. http://citeseer.ist.psu.edu/holmes99generating.html
Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63–91.
Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
参见----------See Also----------
Weka_classifiers
Weka_classifiers
实例----------Examples----------
M5Rules(mpg ~ ., data = mtcars)
m <- PART(Species ~ ., data = iris)
m
summary(m)
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注：
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发表于 2012-9-29 20:56:17
