**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 Filter for Sentiment Analysis 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. | ||

CAAR | Regression, Ensemble learning | Context Aware Case-Based Regression Learner | ||

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 | ||

CVAttributeEval | Attribute selection | An Variation degree Algorithm to explore the space of attributes. | ||

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. | ||

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. | ||

GPAttributeGeneration | Classification, Preprocessing | Genetic Programming Attribute Generation | ||

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 | ||

J48graft | Classification | Class for generating a grafted (pruned or unpruned) C4.5 decision tree | ||

JDBCDriversDummyPackage | Misc | Dummy package that provides a place to drop JDBC driver jar files so that they get loaded by the system. | ||

LVQ | Clustering | Cluster data using the Learning Vector Quantization algorithm. | ||

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 | ||

MTreeClusterer | Clustering | MTree Clusterer | ||

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 | ||

PCP | Visualization | Parallel Coordinates Plot | ||

PSOSearch | Attribute selection | An implementation of the Particle Swarm Optimization (PSO) algorithm to explore the space of attributes. | ||

RBFNetwork | Classification/regression | Classes that implement radial basis function networks. | ||

RPlugin | R integration | Execute R Scripts | ||

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 | ||

SMOTE | Preprocessing | Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE). | ||

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). | ||

SSF | Attribute Selection | Simplified Silhouette Filter | ||

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. | ||

SparseGenerativeModel | Text classification | Sparse Generative Model | ||

StudentFilters | Preprocessing | Student Filters | ||

TPP | Visualization | Targeted Projection Pursuit | ||

WekaExcel | Converter | WEKA MS Excel loader/saver | ||

WekaODF | Converter | WEKA ODF loader/saver | ||

WekaPyScript | Classification | WekaPyScript | ||

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 | ||

anonymizationPackage | Preprocessing | A Filter to apply k-anonymization and l-diversity | ||

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. | ||

baggedLocalOutlierFactor | Outlier | Filter implementing the Bagged LOF outlier/anomaly detection algorithm. | ||

bayesianLogisticRegression | Text classification | Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors | ||

bestFirstTree | Classification | Class for building a best-first decision tree classifier. | ||

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 | ||

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. | ||

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. | ||

jfreechartOffscreenRenderer | KnowledgeFlow | Offscreen chart renderer plugin for the Knowledge Flow that uses JFreeChart | ||

jsonFieldExtractor | Knowledge Flow | Extract fields from repeating JSON structures. | ||

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 | ||

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 | ||

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. | ||

userClassifier | Classification/regression | Interactively classify through visual means. | ||

votingFeatureIntervals | Classification | Classification by voting feature intervals. | ||

wavelet | Preprocessing | A filter for wavelet transformation. | ||

wekaDeeplearning4j | Classification/Regression | Weka wrappers for Deeplearning4j | ||

wekaDeeplearning4jCPU | Classification/Regression | Weka wrappers for Deeplearning4j | ||

wekaDeeplearning4jCPULibs | Classification/Regression | CPU native libraries for wekaDeeplearning4j | ||

wekaDeeplearning4jCore | Classification/Regression | Weka wrappers for Deeplearning4j | ||

wekaDeeplearning4jGPU | Classification/Regression | Weka wrappers for Deeplearning4j | ||

wekaDeeplearning4jGPULibs | Classification/Regression | GPU native libraries for wekaDeeplearning4j | ||

wekaPython | Python integration | Provides integration with CPython in Weka. | ||

wekaServer | Server | Simple server for executing Weka tasks. | ||

winnow | Classification | Implements Winnow and Balanced Winnow algorithms by Littlestone |