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Java Programs for Machine Learning
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Copyright (C) 1998-2007 University of Waikato
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web: http://www.cs.waikato.ac.nz/~ml/weka
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=====================================================================
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1. Using one of the graphical user interfaces in Weka
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2. The Weka data format (ARFF)
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3. Using Weka from the command line
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5. The Experiment package (and tutorial)
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7. KnowlodegeFlow tutorial
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8. Bayesian Network Classifiers
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11. Submission of code and bug reports
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1. Using one of the graphical user interfaces in Weka:
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This assumes that the Weka archive that you have downloaded has been
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extracted into a directory containing this README and that you haven't
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used an automatic installer (e.g. the one provided for Windows).
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Weka 3.5 requires Java 1.5 or higher. Depending on your platform you
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may be able to just double-click on the weka.jar icon to run the
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graphical user interfaces for Weka. Otherwise, from a command-line
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(assuming you are in the directory containing weka.jar), type
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or if you are using Windows use
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Using "-jar" overrides your CLASSPATH variable! If you need to
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use classes specified in the CLASSPATH, use the following command
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java -classpath $CLASSPATH:weka.jar weka.gui.Main
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or if you are using Windows use
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javaw -classpath "%CLASSPATH%;weka.jar" weka.gui.Main
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This will start a graphical user interface (weka.gui.Main) from
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which you can select various interfaces, like the SimpleCLI interface
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or the more sophisticated Explorer, Experimenter, and Knowledge Flow
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interfaces. SimpleCLI just acts like a simple command shell. The
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Explorer is currently the main interface for data analysis using
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Weka. The Experimenter can be used to compare the performance of
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different learning algorithms across various datasets. The Knowledge
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Flow provides a component-based alternative to the Explorer interface.
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Example datasets that can be used with Weka are in the sub-directory
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called "data", which should be located in the same directory as this
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The Weka user interfaces provide extensive built-in help facilities
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(tool tips, etc.). Documentation for the Explorer can be found in
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ExplorerGuide.pdf (also in the same directory as this
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You can also start the GUIChooser from within weka.jar:
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java -classpath weka.jar:$CLASSPATH weka.gui.GUIChooser
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or if you are using Windows use
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javaw -classpath weka.jar;$CLASSPATH weka.gui.GUIChooser
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2. The Weka data format (ARFF):
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-------------------------------
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Datasets for WEKA should be formatted according to the ARFF
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format. (However, there are several converters included in WEKA that
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can convert other file formats to ARFF. The Weka Explorer will use
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these automatically if it doesn't recognize a given file as an ARFF
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file.) Examples of ARFF files can be found in the "data" subdirectory.
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What follows is a short description of the file format. A more
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complete description is available from the Weka web page.
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A dataset has to start with a declaration of its name:
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followed by a list of all the attributes in the dataset (including
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the class attribute). These declarations have the form
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@attribute attribute_name specification
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If an attribute is nominal, specification contains a list of the
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possible attribute values in curly brackets:
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@attribute nominal_attribute {first_value, second_value, third_value}
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If an attribute is numeric, specification is replaced by the keyword
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numeric: (Integer values are treated as real numbers in WEKA.)
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@attribute numeric_attribute numeric
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In addition to these two types of attributes, there also exists a
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string attribute type. This attribute provides the possibility to
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store a comment or ID field for each of the instances in a dataset:
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@attribute string_attribute string
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After the attribute declarations, the actual data is introduced by a
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tag, which is followed by a list of all the instances. The instances
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are listed in comma-separated format, with a question mark
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representing a missing value.
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Comments are lines starting with % and are ignored by Weka.
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3. Using Weka from the command line:
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------------------------------------
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If you want to use Weka from your standard command-line interface
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(e.g. bash under Linux):
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a) Set WEKAHOME to be the directory which contains this README.
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b) Add $WEKAHOME/weka.jar to your CLASSPATH environment variable.
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c) Bookmark $WEKAHOME/doc/packages.html in your web browser.
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Alternatively you can try using the SimpleCLI user interface available
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from the GUI chooser discussed above.
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In the following, the names of files assume use of a unix command-line
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with environment variables. For other command-lines (including
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SimpleCLI) you should substitute the name of the directory where
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weka.jar lives for $WEKAHOME. If your platform uses something other
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character than / as the path separator, also make the appropriate
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java weka.classifiers.trees.J48 -t $WEKAHOME/data/iris.arff
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This prints out a decision tree classifier for the iris dataset
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and ten-fold cross-validation estimates of its performance. If you
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don't pass any options to the classifier, WEKA will list all the
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available options. Try:
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java weka.classifiers.trees.J48
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The options are divided into "general" options that apply to most
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classification schemes in WEKA, and scheme-specific options that only
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apply to the current scheme---in this case J48. WEKA has a common
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interface to all classification methods. Any class that implements a
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classifier can be used in the same way as J48 is used above. WEKA
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knows that a class implements a classifier if it extends the
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Classifier class in weka.classifiers. Almost all classes in
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weka.classifiers fall into this category. Try, for example:
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java weka.classifiers.bayes.NaiveBayes -t $WEKAHOME/data/labor.arff
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Here is a list of some of the classifiers currently implemented in
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a) Classifiers for categorical prediction:
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weka.classifiers.lazy.IBk: k-nearest neighbour learner
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weka.classifiers.trees.J48: C4.5 decision trees
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weka.classifiers.rules.PART: rule learner
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weka.classifiers.bayes.NaiveBayes: naive Bayes with/without kernels
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weka.classifiers.rules.OneR: Holte's OneR
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weka.classifiers.functions.SMO: support vector machines
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weka.classifiers.functions.Logistic: logistic regression
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weka.classifiers.meta.AdaBoostM1: AdaBoost
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weka.classifiers.meta.LogitBoost: logit boost
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weka.classifiers.trees.DecisionStump: decision stumps (for boosting)
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b) Classifiers for numeric prediction:
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weka.classifiers.functions.LinearRegression: linear regression
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weka.classifiers.trees.M5P: model trees
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weka.classifiers.rules.M5Rules: model rules
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weka.classifiers.lazy.IBk: k-nearest neighbour learner
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weka.classifiers.lazy.LWL: locally weighted learning
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Next to classification schemes, there is some other useful stuff in
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WEKA. Association rules, for example, can be extracted using the
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Apriori algorithm. Try
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java weka.associations.Apriori -t $WEKAHOME/data/weather.nominal.arff
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There are also a number of tools that allow you to manipulate a
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dataset. These tools are called filters in WEKA and can be found
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weka.filters.unsupervised.attribute.Discretize: discretizes numeric data
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weka.filters.unsupervised.attribute.Remove: deletes/selects attributes
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java weka.filters.supervised.attribute.Discretize -i
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$WEKAHOME/data/iris.arff -c last
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In terms of database connectivity, you should be able to use any
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database with a Java JDBC driver. When using classes that access a
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database (e.g. the Explorer), you will probably want to create a
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properties file that specifies which JDBC drivers to use, where to
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find the database, and specify a mapping for the data types. This file
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should reside in your home directory or the current directory and be
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called "DatabaseUtils.props". An example is provided in
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weka/experiment (you need to expand weka.jar to be able to look a this
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file). Note that the settings in this file are used unless they are
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overidden by settings in the DatabaseUtils.props file in your home
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directory or the current directory (in that order).
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There are also example DatabaseUtils.props files for several common
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databases available (also in weka/experiment):
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* HSQLDB: DatabaseUtils.props.hsql
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* MS SQL Server 2000: DatabaseUtils.props.mssqlserver
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* MS SQL Server 2005 Express Edition: DatabaseUtils.props.mssqlserver2005
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* MySQL: DatabaseUtils.props.mysql
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* ODBC: DatabaseUtils.props.odbc
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* Oracle: DatabaseUtils.props.oracle
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* PostgreSQL: DatabaseUtils.props.postgresql
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5. The Experiment package (and tutorial):
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-----------------------------------------
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There is support for running experiments that involve evaluating
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classifiers on repeated randomizations of datasets, over multiple
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datasets (you can do much more than this, besides). The classes for
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this reside in the weka.experiment package. The basic architecture is
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that a ResultProducer (which generates results on some randomization
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of a dataset) sends results to a ResultListener (which is responsible
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for stating whether it already has the result, and otherwise storing
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Example ResultListeners include:
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weka.experiment.CSVResultListener: outputs results as
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comma-separated-value files.
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weka.experiment.InstancesResultListener: converts results into a set
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weka.experiment.DatabaseResultListener: sends results to a database
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Example ResultProducers include:
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weka.experiment.RandomSplitResultProducer: train/test on a % split
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weka.experiment.CrossValidationResultProducer: n-fold cross-validation
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weka.experiment.AveragingResultProducer: averages results from another
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weka.experiment.DatabaseResultProducer: acts as a cache for results,
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storing them in a database.
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The RandomSplitResultProducer and CrossValidationResultProducer make
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use of a SplitEvaluator to obtain actual results for a particular
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split, provided are ClassifierSplitEvaluator (for nominal
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classification) and RegressionSplitEvaluator (for numeric
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classification). Each of these uses a Classifier for actual results
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So, you might have a DatabaseResultListener, that is sent results from
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an AveragingResultProducer, which produces averages over the n results
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produced for each run of an n-fold CrossValidationResultProducer,
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which in turn is doing nominal classification through a
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ClassifierSplitEvaluator, which uses OneR for prediction. Whew. But
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you can combine these things together to do pretty much whatever you
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want. You might want to write a LearningRateResultProducer that splits
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a dataset into increasing numbers of training instances.
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To run a simple experiment from the command line, try:
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java weka.experiment.Experiment -r -T datasets/UCI/iris.arff \
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-D weka.experiment.InstancesResultListener \
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-P weka.experiment.RandomSplitResultProducer -- \
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-W weka.experiment.ClassifierSplitEvaluator -- \
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-W weka.classifiers.rules.OneR
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(Try "java weka.experiment.Experiment -h" to find out what these
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If you have your results as a set of instances, you can perform paired
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t-tests using weka.experiment.PairedTTester (use the -h option to find
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out what options it needs).
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However, all this is much easier if you use the Experimenter GUI.
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Check out the tutorial at: $WEKAHOME/ExperimenterTutorial.pdf
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A guide on how to use the WEKA Explorer is in
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$WEKAHOME/ExplorerGuide.pdf. For an explanation on how to use the
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other user interfaces in WEKA you might want to take a look at the
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book "Data Mining" by Witten and Frank (2005) (see our web page).
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7. KnowledgeFlow Tutorial:
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A tutorial on how to use the KnowledgeFlow is in
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$WEKAHOME/KnowledgeFlowTutorial.pdf.
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8. Bayesian Network Classifiers:
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Information about the Bayesian Network classifiers in Weka, background
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theory as well as useage, can be found in the
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$WEKAHOME/BayesianNetClassifiers.pdf.
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The source code for WEKA is in $WEKAHOME/weka-src.jar. To expand it,
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use the jar utility that's in every Java distribution (or any file
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archiver that can handle ZIP files).
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Refer to the web page for a list of contributors:
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http://www.cs.waikato.ac.nz/~ml/weka/
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11. Call for code and bug reports:
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---------------------------------
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If you have implemented a learning scheme, filter, application,
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visualization tool, etc., using the WEKA classes, and you think it
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should be included in WEKA, send us the code, and we can potentially
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put it in the next WEKA distribution.
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The conditions for new classifiers (schemes in general) are that,
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firstly, they have to be published in the proceedings of a renowned
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conference (e.g., ICML) or as an article of respected journal (e.g.,
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Machine Learning) and, secondly, that they outperform other standard
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schemes (e.g., J48/C4.5).
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If you find any bugs, send a bug report to the wekalist mailing list.
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WEKA is distributed under the GNU public license. Please read
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wekadocs/README
