Weka 3.7.1 教程：命令行与GUI指南
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"Weka Manual_3_7_1 是一本关于Weka 3.7.1版本的英文使用手册，由Remco Bouckaert、Eibe Frank、Mark Hall、Richard Kirkby、Peter Reutemann、Alex Seewald和David Scuse等人编写，发布日期为2010年1月11日。该手册遵循GNU General Public License version 2的许可协议。手册包含了对Weka命令行界面和图形用户界面的详细介绍，旨在帮助用户理解和使用这个强大的数据挖掘工具。"
Weka是一款开源的数据挖掘和机器学习软件，主要由新西兰怀卡托大学开发。在本手册中，它详细介绍了如何使用Weka进行数据分析和模式识别。
I 部分主要关注命令行界面（Command-line Interface）：
1. 命令行基础：这部分引导用户了解命令行的基本概念，包括数据集（Dataset）、分类器（Classifier）、过滤器（Filter）和分类器包（weka.classifiers）。
2. 数据集：数据集是分析的基础，可以是.arff文件，包含属性和实例。
3. 分类器：Weka提供了多种预定义的分类算法，如朴素贝叶斯、决策树、支持向量机等，用户可以通过命令行调用并配置这些分类器。
4. 过滤器和分类器包：Weka提供了丰富的预处理工具（weka.filters），用于数据清洗、转换和选择；分类器包则包含各种机器学习算法。
5. 示例：手册给出了一些实际的命令行操作示例，帮助用户上手实践。
II 部分涉及图形用户界面（Graphical User Interface, GUI）：
1. 启动Weka：指导用户如何启动Weka的GUI版本。
2. Simple CLI：简单命令行界面，提供了基本的命令执行功能。
3. 命令操作：描述了在Simple CLI中输入命令的方式，包括命令重定向和命令补全。
4. Explorer：这是Weka的主要工作环境，分为多个部分，如Section Tabs、Status Box、Log Button、WEKA Status Icon和Graphical output。
- Section Tabs包括选择数据、预处理、构建模型、评估和视觉化等步骤。
- Status Box显示程序状态，Log Button记录操作日志。
- Graphical output允许用户以图表形式查看分析结果。
5. 预处理：介绍如何加载数据，以及数据预处理的方法，如数据清洗和特征选择。
Weka Manual_3_7_1为用户提供了全面的指南，无论是在命令行下还是通过图形界面，都能有效地利用Weka进行数据探索、特征工程、模型训练和评估。用户可以根据手册中的指示，逐步学习并掌握这个强大的数据挖掘工具。
16 CHAPTER 1. A COMMAND-LINE PRIMER
1.2.2 Classifier
Any lear ning algorithm in WE KA is derived from the abstract weka.classifiers.Classifier
class. Surprisingly little is needed for a basic classifier: a routine which gen-
erates a classifier model from a training dataset (= buildClassifier) and
another routine which evaluates the generated model on an unseen test da taset
(= classifyInstance), or generates a probability distribution for all classes
(= distributionForInstance).
A classifier model is an ar bitrary complex mapping from all-but-one dataset
attributes to the class attribute. The specific form and creation of this map-
ping, or model, differs from classifier to classifier. For e xample, ZeroR’s (=
weka.classifiers.rules.ZeroR) model just consists of a single value: the
most common class, or the median of all numeric values in case of predicting a
numer ic value (= regression learning). ZeroR is a trivia l classifier, but it gives a
lower bound on the per fo rmance of a g iven dataset which should be significantly
improved by more complex classifiers. As such it is a reasonable test on how
well the class can b e predicted without considering the other attributes.
Later, we will explain how to interpret the output from classifiers in detail –
for now just focus on the Correctly Classified Instances in the section Stratified
cross-validation and notice how it improves from ZeroR to J48:
java weka.classifiers.rules.ZeroR -t weather.arff
java weka.classifiers.trees.J48 -t weather.arff
There are various approaches to determine the performance of classifiers. The
performance can most simply be measured by counting the proportion of cor-
rectly predicted examples in an unseen test dataset. This value is the accuracy,
which is also 1-ErrorRate. Both terms are used in literature.
The simplest case is using a training set and a test set which are mutually
independent. This is referred to as hold-out estimate. To estimate variance in
these performance estimates, hold-out estimates may be computed by repeatedly
resampling the same dataset – i.e. randomly reordering it and then splitting it
into training and test sets with a specific proportio n of the examples, collecting
all estimates on test data and computing average and standard deviation of
accuracy.
A more elaborate method is cross-validation. Here, a number of folds n is
sp e c ified. The dataset is randomly reordered and then split into n folds o f equal
size. In each iter ation, one fold is used for testing and the other n- 1 folds are
used for training the classifier. The test results are collected a nd average d over
all folds. This gives the cross-validation estimate of the accuracy. The folds can
be purely random o r slig htly modified to create the same class distributions in
each fold as in the complete data set. In the latter case the cross-validation is
called stratified. Leave-one-out (= loo) cross -validation signifies that n is equal
to the number of examples. Out of necessity, loo cv has to be non-stratified,
i.e. the class distributions in the test set are not related to those in the training
data. Therefore loo cv tends to give less reliable results. However it is still
quite useful in dealing with small datas e ts since it utilizes the greatest amount
of training data from the dataset.
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