Nearest Neighbors


k-Nearest Neighbors (kNN) learner



  • Data

    A data set

  • Preprocessor

    Preprocessed data


  • Learner

    A kNN learning algorithm with settings as specified in the dialog.

  • kNN Classifier

    Trained classifier (a subtype of Classifier). Signal kNN Classifier sends data only if the learning data (signal Data is present).


  1. A name under which it will appear in other widgets. The default name is “kNN”.
  2. You can set the Number of neighbors.
  3. The Metrics you can use are:
  4. You can assign weight to the contributions of the neighbors. The Weights you can use are:
    • Uniform: all points in each neighborhood are weighted equally.
    • Distance: closer neighbors of a query point have a greater influence than the neighbors further away.
  5. Produce a report.
  6. When you change one or more settings, you need to click Apply, which will put a new learner in the output and, if the training examples are given, construct a new classifier and output it as well. Changes can also be applied automatically by clicking the box on the left side of the Apply button.


This schema compares the results of k-Nearest neighbors with the default classifier, which always predicts the majority class.