IMPORTANT: make sure there are no old versions of Weka (<3.7.2) in your CLASSPATH before starting Weka
java -jar weka.jar
For a command line package manager type:
java weka.core.WekaPackageManager -h
java weka.Run [algorithm name]Substring matching is also supported. E.g. try:java weka.Run Bayes
| AffectiveTweets | Text classification | Text Filters for Analyzing Sentiment and Emotions of Tweets | ||
| AnDE | Classification | Averaged N-Dependence Estimators (includes A1DE and A2DE) | ||
| AnalogicalModeling | Classification | Analogical Modeling | ||
| ArabicStemmers_LightStemmers | Preprocessing | Arabic Stemmer / Light Stemmer | ||
| Auto-WEKA | Classification, Regression, Attribute Selection | Automatically find the best model and parameters for a dataset. | ||
| BANGFile | Clustering | BANG-File Clustering | ||
| CAIRAD | Classification | CAIRAD: A Co-appearance based Analysis for Incorrect Records and Attribute-values Detection | ||
| CFWNB | Classification | Contructs Correlation-based Feature Weighted Naive Bayes (CFWNB) | ||
| CHIRP | Classification | CHIRP: A new classifier based on Composite Hypercubes on Iterated Random Projections | ||
| CLOPE | Clustering | CLOPE: a fast and effective clustering algorithm for transactional data | ||
| CSForest | Classification | CSForest | ||
| CVAttributeEval | Attribute selection | An Variation degree Algorithm to explore the space of attributes. | ||
| DMI | Preprocessing | DMI | ||
| DMNBtext | Text classification | Class for building and using a Discriminative Multinomial Naive Bayes classifier | ||
| DTNB | Classification | Class for building and using a decision table/naive Bayes hybrid classifier. | ||
| DatasetCharacteristicsExtractor | Preprocessing, Experimenter | Class for extracting the main descriptive characteristics of a dataset based on WEKA's simplest classifier, ZeroR. | ||
| DilcaDistance | Distance | Learning distance measure for categorical data | ||
| DistributionBasedBalance | Preprocessing | Distribution-based balancing of datasets | ||
| EAR4 | Regression, Ensemble learning | Case-Based Regression Learner | ||
| EBMC | Classification | Efficient Bayesian Multivariate Classifier | ||
| EMImputation | Preprocessing | Replaces missing numeric values using Expectation Maximization with a multivariate normal model. | ||
| EvolutionarySearch | Attribute selection | An Evolutionary Algorithm (EA) to explore the space of attributes. | ||
| ForExPlusPlus | Classification | ForEx++: A New Framework for Knowledge Discovery from Decision Forests | ||
| ForestPA | Classification | ForestPA: Constructs a Decision Forest by Penalizing Attributes used in Previous Trees. | ||
| GPAttributeGeneration | Classification, Preprocessing | Genetic Programming Attribute Generation | ||
| GenClustPlusPlus | Clustering | GenClust++ | ||
| HMM | Classification, Multiinstance, Sequence | Hidden Markov Model | ||
| IBkLG | Classification | Log and Gaussian kernel for K-NN | ||
| IPCP | Visualization | Interative Parallel Coordinates Plot | ||
| IWSS | Attribute selection | Incremental Wrapper Subset Selection | ||
| IWSSembeddedNB | Attribute selection | Incremental Wrapper Subset Selection with embedded NB classifier | ||
| J48Consolidated | Classification | Class for generating a pruned or unpruned C45 consolidated tree | ||
| J48PartiallyConsolidated | Classification, Ensemble learning | Class for generating a Partially Consolidated Tree-Bagging (PCTBagging) multiple classifier. | ||
| J48graft | Classification | Class for generating a grafted (pruned or unpruned) C4.5 decision tree | ||
| JCDT | Classification, Regression | Java Credal Decision Tree (JCDT) | ||
| JCHAIDStar | Classification | Class for generating a decision tree based on the CHAID* algorithm | ||
| JDBCDriversDummyPackage | Misc | Dummy package that provides a place to drop JDBC driver jar files so that they get loaded by the system. | ||
| KEELLoader | Converter | Reads a source that is in KEEL (Knowledge Extraction based on Evolutionary Learning (http://www.keel.es/)) format. | ||
| LVQ | Clustering | Cluster data using the Learning Vector Quantization algorithm. | ||
| LW | Classification | Large Width Classification | ||
| LibLINEAR | Classification | A wrapper class for the liblinear classifier | ||
| LibSVM | Classification, Regression | A wrapper class for the libsvm tools | ||
| MODLEM | Classification, Ensemble learning | MODLEM rule algorithm | ||
| MultiObjectiveEvolutionaryFuzzyClassifier | Classification | MultiObjectiveEvolutionaryFuzzyClassifier | ||
| MultiObjectiveEvolutionarySearch | Attribute selection | An Multi-objective Evolutionary Algorithm (MOEA) to explore the attribute space. | ||
| NNge | Classification | Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules) | ||
| OpenmlWeka | Classification, Experimenter | Openml Weka | ||
| OptimizedForest | Classification | OptimizedForest | ||
| PSOSearch | Attribute selection | An implementation of the Particle Swarm Optimization (PSO) algorithm to explore the space of attributes. | ||
| PairwiseConsistencyAttributeEval | Attribute selection | Attribute evaluator that evaluates the worth of an attribute i by adding the consistency rates of the attribute subsets composed of attribute i and each of the other attributes. | ||
| PairwiseCorrelationAttributeEval | Attribute selection | Attribute evaluator that evaluates the worth of an attribute i by computing the mean of the worths (using CfsSubsetEval) of the attribute subsets composed of attribute i and each of the other attributes. | ||
| RBFNetwork | Classification/regression | Classes that implement radial basis function networks. | ||
| RPlugin | R integration | Execute R scripts and learning algorithms | ||
| RankCorrelation | Metrics | Rank Correlation Evaluation Metrics | ||
| RankerByDTClassification | Classification, Visualization | Ranker Based on Decision Tree Classification | ||
| RerankingSearch | Attribute selection | Meta-Search algorithm which performs a Hybrid feature selection based on re-ranking | ||
| Rseslib | Classification | Rough Sets, Rule Induction, Neural Net and Analogy-Based Reasoning | ||
| SMOTE | Preprocessing | Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE). | ||
| SPAARC | Classification | SPAARC: Constructs a Decision Tree using Split-Point Sampling and Node Attribute Subsampling. | ||
| SPMFWrapper | Associations | SPMFWrapper | ||
| SPegasos | Classification | Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). | ||
| SVMAttributeEval | Attribute selection | Evaluates the worth of an attribute by using an SVM classifier. | ||
| SelfOrganizingMap | Clustering | Cluster data using the Kohonen's Self-Organizing Map algorithm. | ||
| SmoothPrivateForest | Classification | Smooth Private Forest for Differential Privacy | ||
| SparseGenerativeModel | Text classification | Sparse Generative Model | ||
| StudentFilters | Preprocessing | Student Filters | ||
| SysFor | Classification | SysFor: Systematically Developed Forest of Multiple Decision Trees. | ||
| TPP | Visualization | Targeted Projection Pursuit | ||
| WekaExcel | Converter | WEKA MS Excel loader/saver | ||
| WekaODF | Converter | WEKA ODF loader/saver | ||
| WekaPyScript | Classification | WekaPyScript | ||
| WiSARD | Classification | multi-class classifier using the WiSARD weightless neural network model. | ||
| XMeans | Clustering | Cluster data using the X-means algorithm. | ||
| alternatingDecisionTrees | Classification | Binary-class alternating decision trees and multi-class alternating decision trees. | ||
| alternatingModelTrees | Regression | Alternating Model Trees | ||
| arxAnonymizer | Preprocessing | ARX Anonymization Filter | ||
| associationRulesVisualizer | Visualization | A visualization component for displaying association rules that uses a modified version of the Association Rules Viewer from DESS IAGL of Lille. | ||
| attributeSelectionSearchMethods | Attribute selection | Four search methods for attribute selection: ExhaustiveSearch, GeneticSearch, RandomSearch and RankSearch. | ||
| bayesianLogisticRegression | Text classification | Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors | ||
| bestFirstTree | Classification | Class for building a best-first decision tree classifier. | ||
| calibrationCurve | Visualization | VisualizePlugin for plotting class probability calibration curves | ||
| cascadeKMeans | Clustering | k-means clustering with automatic selection of k | ||
| cassandraConverters | Converters | Loader and saver for the cassandra NoSQL database | ||
| chiSquaredAttributeEval | Attribute selection | Attribute evaluator that evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class. | ||
| citationKNN | Multi-instance learning | Modified version of the Citation kNN multi instance classifier | ||
| classAssociationRules | Associations | Class association rules algorithms (including an implementation of the CBA algorithm). | ||
| classificationViaClustering | Classification | A simple meta-classifier that uses a clusterer for classification. | ||
| classificationViaRegression | Classification | Class for doing classification using regression methods. | ||
| classifierBasedAttributeSelection | Attribute selection | A subset evaluator and an attribute evaluator for evaluating the merit of subsets and single attributes respectively using a classifier. | ||
| classifierErrors | Visualization | A visualization component for displaying errors from numeric schemes using the JMathTools library. | ||
| clojureClassifier | Classification | Wrapper classifiers for classifiers implemented in the Clojure programming language | ||
| complementNaiveBayes | Classification | Class for building and using a Complement class Naive Bayes classifier. | ||
| conjunctiveRule | Classification | This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. | ||
| consistencySubsetEval | Attribute selection | Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. | ||
| costSensitiveAttributeSelection | Attribute selection | Two meta attribute selection evaluators (one attribute-based and the other subset-based) for performing cost-sensitive attribute selection. | ||
| dagging | Ensemble learning | This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. | ||
| decorate | Ensemble learning | DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples | ||
| denormalize | Preprocessing | An instance filter that collapses instances with a common grouping ID value into a single instance. | ||
| discriminantAnalysis | Classification | Classes for linear and quadratic discriminant analysis | ||
| distributedWekaBase | Distributed | Generic configuration classes and distributed map/reduce type tasks for Weka | ||
| distributedWekaHadoop | Distributed | Hadoop wrappers for Weka | ||
| distributedWekaHadoop2 | Distributed | Hadoop 2 wrappers for Weka | ||
| distributedWekaHadoop2Libs | Distributed | Hadoop 2.x libraries for distributedWekaHadoop | ||
| distributedWekaHadoopCore | Distributed | Core Hadoop wrappers for Weka | ||
| distributedWekaHadoopLibs | Distributed | Hadoop 1.x libraries for distributedWekaHadoop | ||
| distributedWekaSpark | Distributed | Spark wrappers for Weka | ||
| distributedWekaSpark2Dev | Distributed | Spark 2.x wrappers for Weka | ||
| distributedWekaSpark3Dev | Distributed | Spark 3.x wrappers for Weka | ||
| distributedWekaSparkDev | Distributed | Spark wrappers for Weka | ||
| dualPerturbAndCombine | Classification and regression | Class for building and using classification and regression trees based on the closed-form dual perturb and combine algorithm. | ||
| elasticNet | Regression | An implementation of the elastic net method for linear regression | ||
| ensembleLibrary | Ensemble learning | Manages a libary of ensemble classifiers | ||
| ensemblesOfNestedDichotomies | Ensemble learning | A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. | ||
| extraTrees | Classification | Package for generating a single Extra-Tree | ||
| fastCorrBasedFS | Attribute selection | Feature selection method based on correlation measureand relevance and redundancy analysis | ||
| filteredAttributeSelection | Attribute selection | Two meta attribute selection evaluators (one attribute-based and the other subset-based) for filtering data before performing attribute selection. | ||
| fourierTransform | Preprocessing | Filters for transforming using the fast fourier transform | ||
| functionalTrees | Classification | Classifier for learning Functional Trees | ||
| fuzzyLaticeReasoning | Classification | The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy Lattices for creating a Reasoning Environment | ||
| fuzzyUnorderedRuleInduction | Classification | Fuzzy Unordered Rule Induction Algorithm | ||
| gaussianProcesses | Regression | Implements Gaussian Processes for regression without hyperparameter-tuning. | ||
| generalizedSequentialPatterns | Associations | Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set | ||
| grading | Ensemble learning | Implements Grading. The base classifiers are "graded". | ||
| graphgram | Clustering, Visualization | GraphGram - Visualization for Clusterings | ||
| gridSearch | Classification | Performs a grid search of parameter pairs for the a classifier. | ||
| hiddenNaiveBayes | Classification | Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC | ||
| hiveJDBC | Misc | A package containing the JDBC driver and dependencies for the Apache Hive database, along with a DatabaseUtils.props file for use with Weka. | ||
| hotSpot | Associations | HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. | ||
| hyperPipes | Classification | Class implementing a HyperPipe classifier. | ||
| imageFilters | Preprocessing | A package that contains filters to process image files. | ||
| isolationForest | Outlier | Class for building and using a classifier built on the Isolation Forest anomaly detection algorithm. | ||
| isotonicRegression | Regression | Learns an isotonic regression model. | ||
| iterativeAbsoluteErrorRegression | Regression | A meta learner that fits a regression model to minimize absolute error. | ||
| javaFXScatterPlot3D | Visualization | A visualization component for displaying a 3D scatter plot of the data using JavaFX 3D. | ||
| jfreechartOffscreenRenderer | KnowledgeFlow | Offscreen chart renderer plugin for the Knowledge Flow that uses JFreeChart | ||
| jsonFieldExtractor | Knowledge Flow | Extract fields from repeating JSON structures. | ||
| kerasZoo | Python integration | Provides a wrapper classifier for zoo models available in Keras | ||
| kerasZoo10MonkeysExample | Python integration | Provides a Knowledge Flow template example for training a DL model on the Kaggle 10 monkeys data | ||
| kernelLogisticRegression | Classification | A package that contains a class to train a two-class kernel logistic regression model. | ||
| kfGroovy | KnowledgeFlow | A Knowledge Flow plugin that provides a Knowledge Flow step that wraps around a Groovy script. | ||
| kfKettle | KnowledgeFlow | A Knowledge Flow plugin that serves as a data source for data coming from the Kettle ETL tool. | ||
| kfPMMLClassifierScoring | KnowledgeFlow | A Knowledge Flow plugin that provides a Knowledge Flow step for scoring test sets or instance streams using a PMML classifier. | ||
| largeScaleKernelLearning | Preprocessing | A package that contains filters for large-scale kernel-based learning | ||
| latentSemanticAnalysis | Preprocessing | Performs latent semantic analysis and transformation of the data | ||
| lazyAssociativeClassifier | Classification | Lazy Associative Classifier | ||
| lazyBayesianRules | Classification | Lazy Bayesian Rules Classifier | ||
| leastMedSquared | Regression | Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions. | ||
| levenshteinEditDistance | Distance measure | Computes the Levenshtein edit distance between two strings | ||
| linearForwardSelection | Attribute selection | Extension of BestFirst that takes a restricted number of k attributes into account. | ||
| localOutlierFactor | Outlier | Filter implementing the Local Outlier Factor (LOF) outlier/anomaly detection algorithm. | ||
| logarithmicErrorMetrics | Metrics | Root mean square logarithmic error and mean absolute logarithmic error | ||
| massiveOnlineAnalysis | Data streams | MOA (Massive On-line Analysis). | ||
| metaCost | Classification | This metaclassifier makes its base classifier cost-sensitive using Pedro Domingo's method. | ||
| metaphorSearchMethods | Attribute selection | An implementation of metaphor search methods to explore the space of attributes. | ||
| multiBoostAB | Ensemble learning | Class for boosting a classifier using the MultiBoosting method. | ||
| multiInstanceFilters | Preprocessing | A collection of filters for manipulating multi-instance data. | ||
| multiInstanceLearning | Multi-instance learning | A collection of multi-instance learning classifiers. | ||
| multiLayerPerceptrons | Classification/regression, Preprocessing | This package currently contains classes for training multilayer perceptrons with one hidden layer for classification and regression, and autoencoders. | ||
| multilayerPerceptronCS | Classification | An extension of the standard MultilayerPerceptron classifier in Weka that adds context-sensitive Multiple Task Learning (csMTL) | ||
| multisearch | Classification | MultiSearch Parameter Optimization | ||
| naiveBayesTree | Classification | Class for generating a decision tree with naive Bayes classifiers at the leaves. | ||
| netlibNativeLinux | Linear Algebra | netlib-java wrappers and native libraries for BLAS, LAPACK and ARPACK under Linux | ||
| netlibNativeOSX | Linear Algebra | netlib-java wrappers and native libraries for BLAS, LAPACK and ARPACK under OS X | ||
| netlibNativeOSXarm | Linear Algebra | netlib-java wrappers and native libraries for BLAS, LAPACK and ARPACK under OS X (macOS) for Apple Arm processors | ||
| netlibNativeWindows | Linear Algebra | netlib-java wrappers and native libraries for BLAS, LAPACK and ARPACK under Windows | ||
| newKnowledgeFlowStepExamples | Examples | Example Step implementations for the new Knowledge Flow, as described in the Weka manual | ||
| niftiLoader | Converter | Package for loading a directory with MRI data in NIfTI format into WEKA | ||
| normalize | Preprocessing | An instance filter that normalize instances considering only numeric attributes and ignoring class index | ||
| oneClassClassifier | Classification | Performs one-class classification on a dataset. | ||
| optics_dbScan | Clustering | The OPTICS and DBSCAN clustering algorithms | ||
| ordinalClassClassifier | Classification | Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. | ||
| ordinalLearningMethod | Classification | An implementation of the Ordinal Learning Method (OLM) | ||
| ordinalStochasticDominance | Classification | An implementation of the Ordinal Stochastic Dominance Learner | ||
| paceRegression | Regression | Class for building pace regression linear models and using them for prediction. | ||
| partialLeastSquares | Preprocessing | Partial least squares filter and classifier for performing PLS regression. | ||
| percentageErrorMetrics | Metrics | Root mean square percentage error and mean absolute percentage error | ||
| predictiveApriori | Associations | Class implementing the predictive apriori algorithm for mining association rules. | ||
| prefuseGraph | Visualization | A visualization component for displaying graphs that uses the prefuse visualization toolkit. | ||
| prefuseGraphViewer | KnowledgeFlow | A Knowledge Flow visualization component for displaying trees and graphs that uses the prefuse visualization toolkit. | ||
| prefuseTree | Visualization | A visualization component for displaying trees that uses the prefuse visualization toolkit. | ||
| probabilisticSignificanceAE | Attribute Selection | Evaluates the worth of an attribute by computing the Probabilistic Significance as a two-way function | ||
| probabilityCalibrationTrees | Probability calibration, Ensemble learning | Probability calibration trees plus ensemble learning using cascade generalization | ||
| raceSearch | Attribute Selection | Races the cross validation error of competing attribute subsets. | ||
| racedIncrementalLogitBoost | Ensemble learning | Classifier for incremental learning of large datasets by way of racing logit-boosted committees. | ||
| realAdaBoost | Ensemble learning | Class for boosting a 2-class classifier using the Real Adaboost method. | ||
| regressionByDiscretization | Regression | A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized. | ||
| ridor | Classification | An implementation of a RIpple-DOwn Rule learner. | ||
| rotationForest | Ensemble learning | Ensembles of decision trees trained on rotated subsamples of the training data. | ||
| sasLoader | Converter | SAS sas7bdat file reader | ||
| scatterPlot3D | Visualization | A visualization component for displaying a 3D scatter plot of the data using Java 3D. | ||
| scriptingClassifiers | Classification | Wrapper classifiers for Jython and Groovy scripting code. | ||
| sequentialInformationalBottleneckClusterer | Clustering | Cluster data using the sequential information bottleneck algorithm. | ||
| simpleCART | Classification | Class implementing minimal cost-complexity pruning. | ||
| simpleEducationalLearningSchemes | Classification | Simple learning schemes for educational purposes (Prism, Id3, IB1 and NaiveBayesSimple). | ||
| snowball-stemmers | Preprocessing | Snowball stemmers | ||
| stackingC | Ensemble learning | Implements StackingC (more efficient version of stacking) | ||
| streamingUnivariateStats | KnowledgeFlow | A Knowledge Flow step to compute summary statistics incrementally | ||
| supervisedAttributeScaling | Preprocessing | A simple filter to rescale attributes to reflect their discriminative power. | ||
| tabuAndScatterSearch | Attribute selection | Search methods contributed by Adrian Pino (ScatterSearchV1, TabuSearch) | ||
| tertius | Associations | Finds rules according to confirmation measure (Tertius-type algorithm) | ||
| thresholdSelector | Classification | A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. | ||
| tigerjython | Scripting | TigerJython | ||
| timeSeriesFilters | Filters, Time Series | Time Series Filters | ||
| timeseriesForecasting | Time series | Time series forecasting environment. | ||
| ultraBoost | Classification | Class to adaptively boost heterogeneous classifiers | ||
| userClassifier | Classification/regression | Interactively classify through visual means. | ||
| vines | Density Estimation | Regular Vines | ||
| votingFeatureIntervals | Classification | Classification by voting feature intervals. | ||
| wavelet | Preprocessing | A filter for wavelet transformation. | ||
| wekaDeeplearning4j | Classification/Regression | Weka wrappers for Deeplearning4j | ||
| wekaPython | Python integration | Provides integration with CPython in Weka. | ||
| wekaRAPIDS | Python integration | Provides integration with RAPIDS in Weka. | ||
| wekaServer | Server | Simple server for executing Weka tasks. | ||
| winnow | Classification | Implements Winnow and Balanced Winnow algorithms by Littlestone |