What is WEKA? | Weka Tutorial


Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code.


  • Collection of ML algorithms – open source Java package
    • https://www.cs.waikato.ac.nz/ml/weka/
  • Schemes for classification include:
    • decision trees, rule learners, naive Bayes, decision tables, locally weighted regression, SVMs, instance-based learners, logistic regression, voted perceptrons, multi-laver perceptron
  • Schemes for numeric prediction include:
    • linear regression, model tree generators, locally weighted regression, instance-based learners, decision tables, multi-laver perceptron
  • Meta-schemes include:
    • Bagging, boosting, stacking, regression via classification, classification via regression, cost sensitive classification
  • Schemes for clustering:
    • EM and Cobweb