Difference between revisions of "Knn"

From Eigenvector Research Documentation Wiki
Jump to navigation Jump to search
imported>Scott
(Outputs)
imported>Scott
(See Also)
Line 45: Line 45:
 
===See Also===
 
===See Also===
  
[[analysis]], [[cluster]], [[plsda]], [[simca]]
+
[[analysis]], [[cluster]], [[dbscan]], [[knnscoredistance]], [[modelselector]], [[plsda]], [[simca]]

Revision as of 09:04, 21 September 2011

Purpose

K-nearest neighbor classifier.

Synopsis

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
pclass = knn(xref,xtest,k,options) %apply model to xtest
pclass = knn(xtest,model,options)

Description

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).

Inputs

  • 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}.

Outputs

  • 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).
  • 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.

Options

  • options = structure array with the following fields :
  • display: [ 'off' | {'on'} ] governs level of display to screen.
  • preprocessing: { [ ] } A cell containing a preprocessing structure or keyword (see PREPROCESS). Use {'autoscale'} to perform autoscaling on reference and test data.
  • 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)

See Also

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