Nearest Neighbors

k-Nearest Neighbors (kNN) learner

Signals

Inputs:

• Data

  A data set

• Preprocessor

  Preprocessed data

Outputs:

• 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).

Description

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:
   • Euclidean
   • Manhattan (the sum of absolute differences for all attributes)
   • Chebyshev (the maximal difference between attributes)
   • Mahalanobis (difference between an attribute and the mean).
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.

Example

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