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K-nearest neighbor classifier.


pclass = knn(xref,xtest,k,options); %make prediction without model
pclass = knn(xref,xtest,options); %use default k
model = knn(xref,k,options) %create model
modelp = knn(xref,model,k,options) %apply model to xtest
modelp = knn(xtest,model,options) %apply model to xtest; predictions (equivalent to pclass) in modelp.classification.mostprobable.
[pclass,closest,votes] = knn(xref,xtest,k,options); %make prediction without model
[pclass,closest,votes] = knn(xref,xtest,options); %use default k
[pclass,closest,votes] = knn(xref,k,options); %self-prediction without model
knn % Launches an Analysis window with KNN as the selected method.

Please note that the recommended way to build and apply a K-nearest neighbor model from the command line is to use the Model Object. Please see this wiki page on building and applying models using the Model Object.


Performs kNN classification where the "k" closest samples in a reference set vote on the class of an unknown sample based on distance to the reference samples. If no majority is found, the unknown is assigned the class of the closest sample (see input options for other no-majority behaviors).


  • xref = a DataSet object of reference data,
  • xtest = a DataSet object or Double containing the unknown test data.

Optional Inputs

  • model = an optional standard KNN model structure which can be passed instead of xref (note order of inputs: (xtest,model) ) to apply model to test data.
  • k = number of components {default = rank of X-block}.


  • pclass = the voted closest class, if a majority of nearest neighbors were of the same class, or the class of the closest sample, if no majority was found (Only returned if xtest is supplied).
  • closest = matrix of samples (rows) by closest neighbor index (columns). Will always have k columns indicating which samples were the closest to the given sample (row).
  • votes = maxtix of samples (rows) by class numbers voted for (columns). Will always have k columns indicating which classes were voted for by each nearest neighbor corresponding to closest matrix.
  • model = if no test data (xtest) is supplied, a standard model structure is returned which can be used with test data in the future to perform a prediction. Note that information about the classification of X-block samples is available in the classification field, described at Standard Model.

For more information on class predictions, see Sample Classification Predictions.


options = structure array with the following fields :

  • display: [ 'off' | {'on'} ] governs level of display to screen.
  • waitbar : [ 'off' | 'on' |{'auto'}] governs display of a waitbar when classifying. 'on' always shows a waitbar, 'off' never shows a waitbar, 'auto' shows a waitbar only when the data is particularly large.
  • preprocessing: { [ ] } A cell containing a preprocessing structure or keyword (see PREPROCESS). Use {'autoscale'} to perform autoscaling on reference and test data.
  • classset : [ 1 ] indicates which class set in xref to use.
  • nomajority: [ 'error' | {'closest'} | class_number ] Behavior when no majority is found in the votes. 'closest' = return class of closest sample. 'error' = give error message. class_number (i.e. any numerical value) = return this value for no-majority votes (e.g. use 0 to return zero for all no-majority votes)
  • predictionrule: [ {'mostprobable'} | 'strict' ] governs which classification prediction statistics appear first in the confusion matrix and confusion table summaries.
  • compression: [{'none'}| 'pca' | 'pls' ] type of data compression to perform on the x-block prior to calculating or applying the KNN model. 'pca' uses a simple PCA model to compress the information. 'pls' uses a pls model.
  • compressncomp: [ 1 ] Number of latent variables (or principal components) to include in the compression model.
  • compressmd: [ 'no' |{'yes'}] Use Mahalanobis Distance corrected

See Also

analysis, cluster, dbscan, knnscoredistance, modelselector, plsda, simca, svmda, EVRIModel_Objects