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    %% Train a _k_-Nearest Neighbor Classifier
% Construct a _k_-nearest neighbor classifier for Fisher's iris data, where
% _k_, the number of nearest neighbors in the predictors, is 5.
%%
% Load Fisher's iris data.
load fisheriris
X = meas;
Y = species;
%%
% |X| is a numeric matrix that contains four petal measurements for 150
% irises.  |Y| is a cell array of character vectors that contains the corresponding
% iris species.
%%
% Train a 5-nearest neighbors classifier. It is good practice to
% standardize noncategorical predictor data.
Mdl = fitcknn(X,Y,'NumNeighbors',5,'Standardize',1)
%%
% |Mdl| is a trained |ClassificationKNN| classifier, and some of its
% properties display in the Command Window.
%%
% To access the properties of |Mdl|, use dot notation.
Mdl.ClassNames
Mdl.Prior
%%
% |Mdl.Prior| contains the class prior probabilities, which are settable using
% the name-value pair argument |'Prior'| in |fitcknn|.  The order of the
% class prior probabilities corresponds to the order of the classes in
% |Mdl.ClassNames|.  By default, the prior probabilities are the 
% respective relative frequencies of the classes in the data.
%%
% You can also reset the prior probabilities after training.  For example,
% set the prior probabilities to 0.5, 0.2, and 0.3 respectively.
Mdl.Prior = [0.5 0.2 0.3];
%%
% You can pass |Mdl| to, for example, <docid:stats_ug.bs85nou> to label new measurements, or 
% <docid:stats_ug.bs85m95> to cross validate the classifier.