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Weka

AI & Machine LearningML/AI / Machine LearningJava
Official docs ↗GitHub ↗

What it is

Weka is a collection of machine learning algorithms for data mining tasks in Java. It provides tools for classification, regression, clustering, association rule mining, and data preprocessing, with both a GUI and programmatic API.

Weka provides an API for loading datasets, applying machine learning algorithms, evaluating models, and exporting results. It supports ARFF, CSV, and database inputs, and integrates preprocessing filters, feature selection, and model evaluation methods.

Installation

<dependency> <groupId>nz.ac.waikato.cms.weka</groupId> <artifactId>weka-stable</artifactId> <version>3.8.6</version> </dependency>

Getting started

The smallest useful thing you can do with it, and what each part means.

Loading a dataset and printing summary
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;

DataSource source = new DataSource("data/iris.arff");
Instances data = source.getDataSet();
if(data.classIndex() == -1) data.setClassIndex(data.numAttributes() - 1);
System.out.println(data);
Loads an ARFF dataset and prints a summary of instances and attributes.
Training a simple classifier
import weka.classifiers.trees.J48;
import weka.classifiers.Evaluation;

J48 tree = new J48();
tree.buildClassifier(data);
Evaluation eval = new Evaluation(data);
eval.crossValidateModel(tree, data, 10, new java.util.Random(1));
System.out.println(eval.toSummaryString());
Trains a J48 decision tree on the dataset and evaluates it using 10-fold cross-validation.

Advanced usage

Where the library earns its place over a simpler alternative.

Data preprocessing with filters
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;

Normalize normalize = new Normalize();
normalize.setInputFormat(data);
Instances normalizedData = Filter.useFilter(data, normalize);
Applies normalization preprocessing to the dataset before training.
Clustering
import weka.clusterers.SimpleKMeans;
SimpleKMeans kMeans = new SimpleKMeans();
kMeans.setNumClusters(3);
kMeans.buildClusterer(data);
Performs k-means clustering on the dataset.
Evaluating classifiers with ROC
eval.evaluateModel(tree, data);
System.out.println(eval.areaUnderROC(1));
Evaluates classifier performance and prints ROC area for the positive class.
Feature selection
import weka.attributeSelection.InfoGainAttributeEval;
import weka.attributeSelection.Ranker;
weka.attributeSelection.AttributeSelection selector = new weka.attributeSelection.AttributeSelection();
selector.setEvaluator(new InfoGainAttributeEval());
selector.setSearch(new Ranker());
selector.SelectAttributes(data);
Performs feature selection using information gain and ranks attributes.

Errors and fixes

The failures you are most likely to hit, and what actually resolves them.

Exception: No class attribute assigned
Ensure the dataset has the class attribute set before training classifiers.
ArrayIndexOutOfBoundsException
Check dataset formatting and ensure ARFF/CSV files are correctly structured.
Exception: Cannot handle string attributes
Convert string attributes to nominal or numeric using preprocessing filters.

Best practices

  • Normalize or standardize data when needed.
  • Use cross-validation to assess model performance reliably.
  • Filter irrelevant or redundant features to improve accuracy.
  • Try multiple algorithms and compare results.
  • Leverage Weka’s evaluation metrics to understand classifier performance.

Background

Why it exists, and what it was reacting to.

Weka was developed at the University of Waikato to provide an accessible platform for teaching, research, and experimentation in machine learning. It offers a wide range of algorithms and evaluation methods, making it popular in academia and for rapid prototyping of ML models in Java.

Related libraries

Others in AI & Machine Learning for Java.

← All Java librariesJava guide