WEKA is a comprehensive toolbench for machine learning and data mining. Its main strengths lie in the classification area, where all current ML approaches -- and quite a few older ones -- have been implemented within a clean, object-oriented Java class hierarchy. Regression, Association Rules and clustering algorithms have also been implemented.

However, WEKA is also quite complex to handle -- amply demonstrated by many questions on the WEKA mailing list. Concerning the graphical user interface, the WEKA development group offers documentation for the Explorer and the Experimenter. However, there is little documentation on using the command line interface to WEKA, although it is essential for realistic learning tasks.

This document serves as a practical introduction to the command line interface. Since there has been a recent reorganization in class hierarchies for WEKA, all examples may only work with versions 3.4.4 and above only (until the next reorganization, that is ;-) Basic concepts and issues can more easily be transferred to earlier versions, but the specific examples may need to be slightly adapted (mostly removing the third class hierarchy level and renaming some classes).

While for initial experiments the included graphical user interface is quite sufficient, for in-depth usage the command line interface is recommended, because it offers some functionality which is not available via the GUI - and uses far less memory. Should you get Out of Memory errors, increase the maximum heap size for your java engine, usually to -Xmx1024M or -Xmx1024m for 1GB. When using newer versions of Weka, Windows users should edit RunWeka.ini to modify the value of maxheap from 256m to 1024m. With older versions of Weka, Windows users should edit RunWeka.bat to add the parameter -Xmx1024M before the -jar option, yielding java -Xmx1024M -jar weka.jar, If you get errors that classes are not found, check your CLASSPATH: does it include weka.jar? You can explicitly set CLASSPATH via the -cp command line option as well.

We will begin by describing basic concepts and ideas. Then, we will describe the weka.filters package, which is used to transform input data, e.g. for preprocessing, transformation, feature generation and so on.

Then we will focus on the machine learning algorithms themselves. These are called Classifiers in WEKA. We will restrict ourselves to common settings for all classifiers and shortly note representatives for all main approaches in machine learning.

Afterwards, practical examples are given. In Appendix A you find an example java program which utilizes various WEKA classes in order to give some functionality which is not yet integrated in WEKA -- namely to output predictions for test instances within a cross-validation. It also outputs the complete class probability distribution.

Finally, in the doc directory of WEKA you find a documentation of all java classes within WEKA. Prepare to use it since this overview is not intended to be complete. If you want to know exactly what is going on, take a look at the mostly well-documented source code, which can be found in weka-src.jar and can be extracted via the jar utility from the Java Development Kit.

If you find any bugs, less comprehensible statements, have comments or want to offer suggestions, please contact me.

Basic concepts


A set of data items, the dataset, is a very basic concept of machine learning. A dataset is roughly equivalent to a two-dimensional spreadsheet or database table. In WEKA, it is implemented by the Instances class. A dataset is a collection of examples, each one of class Instance. Each Instance consists of a number of attributes, any of which can be nominal (= one of a predefined list of values), numeric (= a real or integer number) or a string (= an arbitrary long list of characters, enclosed in "double quotes"). The external representation of an Instances class is an ARFF file, which consists of a header describing the attribute types and the data as comma-separated list. Here is a short, commented example. A complete description of the ARFF file format can be found here.

% This is a toy example, the UCI weather dataset.
% Any relation to real weather is purely coincidental.}}
Comment lines at the beginning of the dataset should give an indication of its source, context and meaning.

@relation golfWeatherMichigan_1988/02/10_14days
Here we state the internal name of the dataset. Try to be as comprehensive as possible.

@attribute outlook {sunny, overcast rainy}
@attribute windy {TRUE, FALSE}
Here we define two nominal attributes, outlook and windy. The former has three values: sunny, overcast and rainy; the latter two: TRUE and FALSE. Nominal values with special characters, commas or spaces are enclosed in 'single quotes'.

@attribute temperature real
@attribute humidity real
These lines define two numeric attributes. Instead of real, integer or numeric can also be used. While double floating point values are stored internally, only seven decimal digits are usually processed.

@attribute play {yes, no}
The last attribute is the default target or class variable used for prediction. In our case it is a nominal attribute with two values, making this a binary classification problem.

The rest of the dataset consists of the token @data, followed by comma-separated values for the attributes -- one line per example. In our case there are five examples.

In our example, we have not mentioned the attribute type string, which defines "double quoted" string attributes for text mining. In recent WEKA versions, date/time attribute types are also supported.

By default, the last attribute is considered the class/target variable, i.e. the attribute which should be predicted as a function of all other attributes. If this is not the case, specify the target variable via -c. The attribute numbers are one-based indices, i.e. -c 1 specifies the first attribute.

Some basic statistics and validation of given ARFF files can be obtained via the main() routine of weka.core.Instances:
 java weka.core.Instances data/soybean.arff
weka.core offers some other useful routines, e.g. converters.C45Loader and converters.CSVLoader, which can be used to import C45 datasets and comma/tab-separated datasets respectively, e.g.:
 java weka.core.converters.CSVLoader data.csv > data.arff
 java weka.core.converters.C45Loader c45_filestem > data.arff


Any learning algorithm in WEKA is derived from the abstract Classifier class. Surprisingly little is needed for a basic classifier: a routine which generates a classifier model from a training dataset (= buildClassifier) and another routine which evaluates the generated model on an unseen test dataset (= classifyInstance), or generates a probability distribution for all classes (= distributionForInstance).

A classifier model is an arbitrary complex mapping from all-but-one dataset attributes to the class attribute. The specific form and creation of this mapping, or model, differs from classifier to classifier. For example, ZeroR's model just consists of a single value: the most common class, or the median of all numeric values in case of predicting a numeric value (= regression learning). ZeroR is a trivial classifier, but it gives a lower bound on the performance of a given dataset which should be significantly improved by more complex classifiers. As such it is a reasonable test on how well the class can be 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 correctly 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 proportion 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 specified. The dataset is randomly reordered and then split into n folds of equal size. In each iteration, one fold is used for testing and the other n-1 folds are used for training the classifier. The test results are collected and averaged over all folds. This gives the cross-validation estimate of the accuracy. The folds can be purely random or slightly modified to create the same class distributions in each fold as in the complete dataset. 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 datasets since it utilizes the greatest amount of training data from the dataset.


The weka.filters package is concerned with classes that transforms datasets -- by removing or adding attributes, resampling the dataset, removing examples and so on. This package offers useful support for data preprocessing, which is an important step in machine learning.

All filters offer the options -i for specifying the input dataset, and -o for specifying the output dataset. If any of these parameters is not given, this specifies standard input resp. output for use within pipes. Other parameters are specific to each filter and can be found out via -h, as with any other class. The weka.filters package is organized into supervised and unsupervised filtering, both of which are again subdivided into instance and attribute filtering. We will discuss each of the four subsection separately.


Classes below weka.filters.supervised in the class hierarchy are for supervised filtering, i.e. taking advantage of the class information. A class must be assigned via -c, for WEKA default behaviour use -c last.


Discretize is used to discretize numeric attributes into nominal ones, based on the class information, via Fayyad & Irani's MDL method, or optionally with Kononeko's MDL method. At least some learning schemes or classifiers can only process nominal data, e.g. rules.Prism; in some cases discretization may also reduce learning time.
 java weka.filters.supervised.attribute.Discretize -i data/iris.arff -o iris-nom.arff -c last
 java weka.filters.supervised.attribute.Discretize -i data/cpu.arff -o cpu-classvendor-nom.arff -c first
NominalToBinary encodes all nominal attributes into binary (two-valued) attributes, which can be used to transform the dataset into a purely numeric representation, e.g. for visualization via multi-dimensional scaling.
 java weka.filters.supervised.attribute.NominalToBinary -i data/contact-lenses.arff -o contact-lenses-bin.arff -c last
Keep in mind that most classifiers in WEKA utilize transformation filters internally, e.g. Logistic and SMO, so you will usually not have to use these filters explicity. However, if you plan to run a lot of experiments, pre-applying the filters yourself may improve runtime performance.


Resample creates a stratified subsample of the given dataset. This means that overall class distributions are approximately retained within the sample. A bias towards uniform class distribution can be specified via -B.
 java weka.filters.supervised.instance.Resample -i data/soybean.arff -o soybean-5%.arff -c last -Z 5
 java weka.filters.supervised.instance.Resample -i data/soybean.arff -o soybean-uniform-5%.arff -c last -Z 5 -B 1
StratifiedRemoveFolds creates stratified cross-validation folds of the given dataset. This means that per default the class distributions are approximately retained within each fold. The following example splits soybean.arff into stratified training and test datasets, the latter consisting of 25% (=1/4) of the data.
 java weka.filters.supervised.instance.StratifiedRemoveFolds -i data/soybean.arff -o soybean-train.arff \
   -c last -N 4 -F 1 -V
 java weka.filters.supervised.instance.StratifiedRemoveFolds -i data/soybean.arff -o soybean-test.arff \
   -c last -N 4 -F 1


Classes below weka.filters.unsupervised in the class hierarchy are for unsupervised filtering, e.g. the non-stratified version of Resample. A class should not be assigned here.


StringToWordVector transforms string attributes into a word vectors, i.e. creating one attribute for each word which either encodes presence or word count (-C) within the string. -W can be used to set an approximate limit on the number of words. When a class is assigned, the limit applies to each class separately. This filter is useful for text mining.

Obfuscate renames the dataset name, all attribute names and nominal attribute values. This is intended for exchanging sensitive datasets without giving away restricted information.

Remove is intended for explicit deletion of attributes from a dataset, e.g. for removing attributes of the iris dataset:
 java weka.filters.unsupervised.attribute.Remove -R 1-2 -i data/iris.arff -o iris-simplified.arff
 java weka.filters.unsupervised.attribute.Remove -V -R 3-last -i data/iris.arff -o iris-simplified.arff


Resample creates a non-stratified subsample of the given dataset, i.e. random sampling without regard to the class information. Otherwise it is equivalent to its supervised variant.
 java weka.filters.unsupervised.instance.Resample -i data/soybean.arff -o soybean-5%.arff -Z 5
RemoveFolds creates cross-validation folds of the given dataset. The class distributions are not retained. The following example splits soybean.arff into training and test datasets, the latter consisting of 25% (=1/4) of the data.
 java weka.filters.unsupervised.instance.RemoveFolds -i data/soybean.arff -o soybean-train.arff -c last -N 4 -F 1 -V
 java weka.filters.unsupervised.instance.RemoveFolds -i data/soybean.arff -o soybean-test.arff -c last -N 4 -F 1
RemoveWithValues filters instances according to the value of an attribute.
 java weka.filters.unsupervised.instance.RemoveWithValues -i data/soybean.arff \
   -o soybean-without_herbicide_injury.arff -V -C last -L 19


Classifiers are at the core of WEKA. There are a lot of common options for classifiers, most of which are related to evaluation purposes. We will focus on the most important ones. All others including classifier-specific parameters can be found via -h, as usual.

specifies the training file (ARFF format)
specifies the test file in (ARFF format). If this parameter is missing, a crossvalidation will be performed (default: 10-fold cv)
This parameter determines the number of folds for the cross-validation. A cv will only be performed if -T is missing.
As we already know from the weka.filters section, this parameter sets the class variable with a one-based index.
The model after training can be saved via this parameter. Each classifier has a different binary format for the model, so it can only be read back by the exact same classifier on a compatible dataset. Only the model on the training set is saved, not the multiple models generated via cross-validation.
Loads a previously saved model, usually for testing on new, previously unseen data. In that case, a compatible test file should be specified, i.e. the same attributes in the same order.
-p <attrib_range>
If a test file is specified, this parameter shows you the predictions and one attribute (0 for none) for all test instances. If no test file is specified, this outputs nothing. In that case, you will have to use callClassifier from Appendix A.
A more detailed performance description via precision, recall, true- and false positive rate is additionally output with this parameter. All these values can also be computed from the confusion matrix.
This parameter switches the human-readable output of the model description off. In case of support vector machines or NaiveBayes, this makes some sense unless you want to parse and visualize a lot of information.

We now give a short list of selected classifiers in WEKA. Other classifiers below weka.classifiers in package overview may also be used. This is more easy to see in the Explorer GUI.
  • trees.J48 A clone of the C4.5 decision tree learner
  • bayes.NaiveBayes A Naive Bayesian learner. -K switches on kernel density estimation for numerical attributes which often improves performance.
  • meta.ClassificationViaRegression -W functions.LinearRegression Multi-response linear regression.
  • functions.Logistic Logistic Regression.
  • functions.SMO Support Vector Machine (linear, polynomial and RBF kernel) with Sequential Minimal Optimization Algorithm due to [Platt, 1998]. Defaults to SVM with linear kernel, -E 5 -C 10 gives an SVM with polynomial kernel of degree 5 and lambda=10.
  • lazy.KStar Instance-Based learner. -E sets the blend entropy automatically, which is usually preferable.
  • lazy.IBk Instance-Based learner with fixed neighborhood. -K sets the number of neighbors to use. IB1 is equivalent to IBk -K 1
  • rules.JRip A clone of the RIPPER rule learner.

Based on a simple example, we will now explain the output of a typical classifier, weka.classifiers.trees.J48. Consider the following call from the command line, or start the WEKA explorer and train J48 on weather.arff:
 java weka.classifiers.trees.J48 -t data/weather.arff -i

J48 pruned tree
outlook = sunny
|   humidity <= 75: yes (2.0)
|   humidity > 75: no (3.0)
outlook = overcast: yes (4.0)
outlook = rainy
|   windy = TRUE: no (2.0)
|   windy = FALSE: yes (3.0)
Number of Leaves  :  5
Size of the tree :  8
The first part, unless you specify -o, is a human-readable form of the training set model. In this case, it is a decision tree. outlook is at the root of the tree and determines the first decision. In case it is overcast, we'll always play golf. The numbers in (parentheses) at the end of each leaf tell us the number of examples in this leaf. If one or more leaves were not pure (= all of the same class), the number of misclassified examples would also be given, after a /slash/

Time taken to build model: 0.05 seconds
Time taken to test model on training data: 0 seconds
As you can see, a decision tree learns quite fast and is evaluated even faster. E.g. for a lazy learner, testing would take far longer than training.

= Error on training data ==
Correctly Classified Instance      14              100      %
Incorrectly Classified Instances    0                0      %
Kappa statistic                     1
Mean absolute error                 0
Root mean squared error             0
Relative absolute error             0      %
Root relative squared error         0      %
Total Number of Instances          14
== Detailed Accuracy By Class ==
TP Rate   FP Rate   Precision   Recall  F-Measure   Class
  1         0          1         1         1        yes
  1         0          1         1         1        no
== Confusion Matrix ==
 a b   <-- classified as
 9 0 | a = yes
 0 5 | b = no
This is quite boring: our classifier is perfect, at least on the training data -- all instances were classified correctly and all errors are zero. As is usually the case, the training set accuracy is too optimistic. The detailed accuracy by class, which is output via -i, and the confusion matrix is similarily trivial.

== Stratified cross-validation ==
Correctly Classified Instances      9               64.2857 %
Incorrectly Classified Instances    5               35.7143 %
Kappa statistic                     0.186
Mean absolute error                 0.2857
Root mean squared error             0.4818
Relative absolute error            60      %
Root relative squared error        97.6586 %
Total Number of Instances          14
== Detailed Accuracy By Class ==
TP Rate   FP Rate   Precision   Recall  F-Measure   Class
  0.778     0.6        0.7       0.778     0.737    yes
  0.4       0.222      0.5       0.4       0.444    no
== Confusion Matrix ==
 a b   <-- classified as
 7 2 | a = yes
 3 2 | b = no
The stratified cv paints a more realistic picture. The accuracy is around 64%. The kappa statistic measures the agreement of prediction with the true class -- 1.0 signifies complete agreement. The following error values are not very meaningful for classification tasks, however for regression tasks e.g. the root of the mean squared error per example would be a reasonable criterion. We will discuss the relation between confusion matrix and other measures in the text.

The confusion matrix is more commonly named contingency table. In our case we have two classes, and therefore a 2x2 confusion matrix, the matrix could be arbitrarily large. The number of correctly classified instances is the sum of diagonals in the matrix; all others are incorrectly classified (class "a" gets misclassified as "b" exactly twice, and class "b" gets misclassified as "a" three times).

The True Positive (TP) rate is the proportion of examples which were classified as class x, among all examples which truly have class x, i.e. how much part of the class was captured. It is equivalent to Recall. In the confusion matrix, this is the diagonal element divided by the sum over the relevant row, i.e. 7/(7+2)=0.778 for class yes and 2/(3+2)=0.4 for class no in our example.

The False Positive (FP) rate is the proportion of examples which were classified as class x, but belong to a different class, among all examples which are not of class x. In the matrix, this is the column sum of class x minus the diagonal element, divided by the rows sums of all other classes; i.e. 3/5=0.6 for class yes and 2/9=0.222 for class no.

The Precision is the proportion of the examples which truly have class x among all those which were classified as class x. In the matrix, this is the diagonal element divided by the sum over the relevant column, i.e. 7/(7+3)=0.7 for class yes and 2/(2+2)=0.5 for class no.

The F-Measure is simply 2*Precision*Recall/(Precision+Recall), a combined measure for precision and recall.

These measures are useful for comparing classifiers. However, if more detailed information about the classifier's predictions are necessary, -p # outputs just the predictions for each test instance, along with a range of one-based attribute ids (0 for none). Let's look at the following example. We shall assume soybean-train.arff and soybean-test.arff have been constructed via weka.filters.supervised.instance.StratifiedRemoveFolds as in a previous example.
 java weka.classifiers.bayes.NaiveBayes -K -t soybean-train.arff -T soybean-test.arff -p 0

0 diaporthe-stem-canker 0.9999672587892333 diaporthe-stem-canker
1 diaporthe-stem-canker 0.9999992614503429 diaporthe-stem-canker
2 diaporthe-stem-canker 0.999998948559035 diaporthe-stem-canker
3 diaporthe-stem-canker 0.9999998441238833 diaporthe-stem-canker
4 diaporthe-stem-canker 0.9999989997681132 diaporthe-stem-canker
5 rhizoctonia-root-rot 0.9999999395928124 rhizoctonia-root-rot
6 rhizoctonia-root-rot 0.999998912860593 rhizoctonia-root-rot
7 rhizoctonia-root-rot 0.9999994386283236 rhizoctonia-root-rot
The values in each line are separated by a single space. The fields are the zero-based test instance id, followed by the predicted class value, the confidence for the prediction (estimated probability of predicted class), and the true class. All these are correctly classified, so let's look at a few erroneous ones.

32 phyllosticta-leaf-spot 0.7789710144361445 brown-spot
39 alternarialeaf-spot 0.6403333824349896 brown-spot
44 phyllosticta-leaf-spot 0.893568420641914 brown-spot
46 alternarialeaf-spot 0.5788190397739439 brown-spot
73 brown-spot 0.4943768155314637 alternarialeaf-spot
In each of these cases, a misclassification occurred, mostly between classes alternarialeaf-spot and brown-spot. The confidences seem to be lower than for correct classification, so for a real-life application it may make sense to output don't know below a certain threshold. WEKA also outputs a trailing newline.

If we had chosen a range of attributes via -p, e.g. -p first-last, the mentioned attributes would have been output afterwards as comma-separated values, in (parantheses). However, the zero-based instance id in the first column offers a safer way to determine the test instances.

Regrettably, -p does not work without test set (in versions before 3.5.8), i.e. for the cross-validation. Although patching WEKA is feasible, it is quite messy and has to be repeated for each new version. Another way to achieve this functionality is callClassifier, which calls WEKA functions from Java and implements this functionality, optionally outputting the complete class probablity distribution also. The output format is the same as above, but because of the cross-validation the instance ids are not in order, which can be remedied via |sort -n.

If we had saved the output of -p in soybean-test.preds, the following call would compute the number of correctly classified instances:
 cat soybean-test.preds | awk '$2=$4&&$0!=""' | wc -l
Dividing by the number of instances in the test set, i.e. wc -l < soybean-test.preds minus one (= trailing newline), we get the training set accuracy.


Usually, if you evaluate a classifier for a longer experiment, you will do something like this (for csh):
 java -Xmx1024m weka.classifiers.trees.J48 -t data.arff -i -k -d J48-data.model >&! J48-data.out &
The -Xmx1024m parameter for maximum heap size ensures your task will get enough memory. There is no overhead involved, it just leaves more room for the heap to grow. -i and -k gives you some additional information, which may be useful, e.g. precision and recall for all classes. In case your model performs well, it makes sense to save it via -d - you can always delete it later! The implicit cross-validation gives a more reasonable estimate of the expected accuracy on unseen data than the training set accuracy. The output both of standard error and output should be redirected, so you get both errors and the normal output of your classifier. The last & starts the task in the background. Keep an eye on your task via top and if you notice the hard disk works hard all the time (for linux), this probably means your task needs too much memory and will not finish in time for the exam. ;-) In that case, switch to a faster classifier or use filters, e.g. for Resample to reduce the size of your dataset or StratifiedRemoveFolds to create training and test sets - for most classifiers, training takes more time than testing.

So, now you have run a lot of experiments -- which classifier is best? Try
 cat *.out | grep -A 3 "Stratified" | grep "^Correctly"
...this should give you all cross-validated accuracies. If the cross-validated accuracy is roughly the same as the training set accuracy, this indicates that your classifiers is presumably not overfitting the training set.

Now you have found the best classifier. To apply it on a new dataset, use e.g.
 java weka.classifiers.trees.J48 -l J48-data.model -T new-data.arff
You will have to use the same classifier to load the model, but you need not set any options. Just add the new test file via -T. If you want, -p first-last will output all test instances with classifications and confidence, followed by all attribute values, so you can look at each error separately.

The following more complex csh script creates datasets for learning curves, i.e. creating a 75% training set and 25% test set from a given dataset, then successively reducing the test set by factor 1.2 (83%), until it is also 25% in size. All this is repeated thirty times, with different random reorderings (-S) and the results are written to different directories. The Experimenter GUI in WEKA can be used to design and run similar experiments.
foreach f ($*)
  set run=1
  while ( $run <= 30 )
    mkdir $run >&! /dev/null
    java weka.filters.supervised.instance.StratifiedRemoveFolds -N 4 -F 1 -S $run -c last -i ../$f -o $run/t_$f
    java weka.filters.supervised.instance.StratifiedRemoveFolds -N 4 -F 1 -S $run -V -c last -i ../$f -o $run/t0$f
    foreach nr (0 1 2 3 4 5)
      set nrp1=$nr
      @ nrp1++
      java weka.filters.supervised.instance.Resample -S 0 -Z 83 -c last -i $run/t$nr$f -o $run/t$nrp1$f
    echo Run $run of $f done.
    @ run++

If meta classifiers are used, i.e. classifiers whose options include classifier specifications - for example, StackingC or ClassificationViaRegression, care must be taken not to mix the parameters. E.g.
 java weka.classifiers.meta.ClassificationViaRegression -W weka.classifiers.functions.LinearRegression -S 1 \
   -t data/iris.arff -x 2
gives us an illegal options exception for -S 1. This parameter is meant for LinearRegression, not for ClassificationViaRegression, but WEKA does not know this by itself. One way to clarify this situation is to enclose the classifier specification, including all parameters, in "double" quotes, like this:
 java weka.classifiers.meta.ClassificationViaRegression -W "weka.classifiers.functions.LinearRegression -S 1" \
   -t data/iris.arff -x 2
However this does not always work, depending on how the option handling was implemented in the top-level classifier. While for Stacking this approach would work quite well, for ClassificationViaRegression it does not. We get the dubious error message that the class weka.classifiers.functions.LinearRegression -S 1 cannot be found.
Fortunately, there is another approach: All parameters given after -- are processed by the first sub-classifier; another -- lets us specify parameters for the second sub-classifier and so on.
 java weka.classifiers.meta.ClassificationViaRegression -W weka.classifiers.functions.LinearRegression \
   -t data/iris.arff -x 2 -- -S 1
In some cases, both approaches have to be mixed, for example:
 java weka.classifiers.meta.Stacking -B "weka.classifiers.lazy.IBk -K 10" \
   -M "weka.classifiers.meta.ClassificationViaRegression -W weka.classifiers.functions.LinearRegression -- -S 1" \
   -t data/iris.arff -x 2
Notice that while ClassificationViaRegression honors the -- parameter, Stacking itself does not. Sadly the option handling for sub-classifier specifications is not yet completely unified within WEKA, but hopefully one or the other approach mentioned here will work.

Appendix A: How to call WEKA from Java

Download the source or the compiled version . Initially, I intended to use this as an illustrative example of calling WEKA routines from Java, but it has become far too complex for that. ;-)

It offers similar functionality to calling classifiers with -p 0, however unlike Evaluation, it outputs the complete class probability for any appropriate classifier. It works with cross-validation as well as a separate test set, so it is possible to get all predictions within the cv. This is not as easy as it sounds, which you will notice if you take a look at the source code.

There are a variety of ways to enable callClassifier to run; hopefully one of them will work for you. Compilation is via javac. If javac is not found, you probably do not have the development kit installed - in that case try downloading callClassifier.class. If classes are not found, check if weka.jar is in the CLASSPATH.
  • Add the current directory (.) to your CLASSPATH (via setenv CLASSPATH .:$WEKAHOME/weka.jar where $WEKAHOME is the directory of your WEKA installation), so that both weka.jar and callClassifier can be found. Start callClassifier only from the current directory:
java callClassifier weka.classifiers.trees.J48 -t iris.arff
  • Create a directory for your own java routines, copy callClassifier to it and add it to the CLASSPATH. Start callClassifier as above.
  • You can also specify the classpath at command line, usually via -classpath or -cp. However, be sure to specify the complete class path, including weka.jar! It is not incremental.
java -classpath .:$WEKAHOME/weka.jar weka.classifiers.trees.J48 -t iris.arff
  • An alternative to using weka.jar is to extract weka.jar into the current directory, via jar. This creates subdirectories for the class hierarchies. In that case, a CLASSPATH to the top-level directory is sufficient. Class hierarchies translate to directories, e.g. weka.classifiers.trees.J48 translates to weka/classifiers/trees/J48.class. You can also extract weka-src.jar into the same directory, so you have the source and compiled WEKA side-by-side in the same directories - great for development and to adapt your own classifiers. In that case you can put callClassifier somewhere useful, for example weka/core and thus incorporate it into WEKA.

A similar program which predates callClassifier is WekaClassifier. It also works for older WEKA versions before 3-4 (those with a separate DistributionClassifier class).