### Class Definition: `ClassificationKNN`

statistics: obj = ClassificationKNN (X, Y)
statistics: obj = ClassificationKNN (…, name, value)

Create a `ClassificationKNN` class object containing a k-Nearest Neighbor classification model.

`obj = ClassificationKNN (X, Y)` returns a ClassificationKNN object, with X as the predictor data and Y containing the class labels of observations in X.

- `X` must be a N×P numeric matrix of input data where rows correspond to observations and columns correspond to features or variables. X will be used to train the kNN model.
- `Y` is N×1 matrix or cell matrix containing the class labels of corresponding predictor data in X. Y can contain any type of categorical data. Y must have same numbers of Rows as X.

`obj = ClassificationKNN (…, name, value)` returns a ClassificationKNN object with parameters specified by `Name-Value` pair arguments. Type `help fitcknn` for more info.

A `ClassificationKNN` object, obj, stores the labelled training data and various parameters for the k-Nearest Neighbor classification model, which can be accessed in the following fields:

Field |  | Description
--- | --- | ---
`obj.X` |  | Unstandardized predictor data, specified as a numeric matrix. Each column of X represents one predictor (variable), and each row represents one observation.
`obj.Y` |  | Class labels, specified as a logical or numeric vector, or cell array of character vectors. Each value in Y is the observed class label for the corresponding row in X.
`obj.NumObservations` |  | Number of observations used in training the ClassificationKNN model, specified as a positive integer scalar. This number can be less than the number of rows in the training data because rows containing `NaN` values are not part of the fit.
`obj.RowsUsed` |  | Rows of the original training data used in fitting the ClassificationKNN model, specified as a numerical vector. If you want to use this vector for indexing the training data in X, you have to convert it to a logical vector, i.e `X = obj.X(logical (obj.RowsUsed), :);`
`obj.Standardize` |  | A boolean flag indicating whether the data in X have been standardized prior to training.
`obj.Sigma` |  | Predictor standard deviations, specified as a numeric vector of the same length as the columns in X. If the predictor variables have not been standardized, then `"obj.Sigma"` is empty.
`obj.Mu` |  | Predictor means, specified as a numeric vector of the same length as the columns in X. If the predictor variables have not been standardized, then `"obj.Mu"` is empty.
`obj.NumPredictors` |  | The number of predictors (variables) in X.
`obj.PredictorNames` |  | Predictor variable names, specified as a cell array of character vectors. The variable names are in the same order in which they appear in the training data X.
`obj.ResponseName` |  | Response variable name, specified as a character vector.
`obj.ClassNames` |  | Names of the classes in the training data Y with duplicates removed, specified as a cell array of character vectors.
`obj.BreakTies` |  | Tie-breaking algorithm used by predict when multiple classes have the same smallest cost, specified as one of the following character arrays: `"smallest"` (default), which favors the class with the smallest index among the tied groups, i.e. the one that appears first in the training labelled data. `"nearest"`, which favors the class with the nearest neighbor among the tied groups, i.e. the class with the closest member point according to the distance metric used. `"nearest"`, which randomly picks one class among the tied groups.
`obj.Prior` |  | Prior probabilities for each class, specified as a numeric vector. The order of the elements in `Prior` corresponds to the order of the classes in `ClassNames`.
`obj.Cost` |  | Cost of the misclassification of a point, specified as a square matrix. `Cost(i,j)` is the cost of classifying a point into class `j` if its true class is `i` (that is, the rows correspond to the true class and the columns correspond to the predicted class). The order of the rows and columns in `Cost` corresponds to the order of the classes in `ClassNames`. The number of rows and columns in `Cost` is the number of unique classes in the response. By default, `Cost(i,j) = 1` if `i != j`, and `Cost(i,j) = 0` if `i = j`. In other words, the cost is 0 for correct classification and 1 for incorrect classification.
`obj.NumNeighbors` |  | Number of nearest neighbors in X used to classify each point during prediction, specified as a positive integer value.
`obj.Distance` |  | Distance metric, specified as a character vector. The allowable distance metric names depend on the choice of the neighbor-searcher method. See the available distance metrics in `knnseaarch` for more info.
`obj.DistanceWeight` |  | Distance weighting function, specified as a function handle, which accepts a matrix of nonnegative distances, and returns a matrix the same size containing nonnegative distance weights.
`obj.DistParameter` |  | Parameter for the distance metric, specified either as a positive definite covariance matrix (when the distance metric is `"mahalanobis"`, or a positive scalar as the Minkowski distance exponent (when the distance metric is `"minkowski"`, or a vector of positive scale values with length equal to the number of columns of X (when the distance metric is `"seuclidean"`. For any other distance metric, the value of `DistParameter` is empty.
`obj.NSMethod` |  | Nearest neighbor search method, specified as either `"kdtree"`, which creates and uses a Kd-tree to find nearest neighbors, or `"exhaustive"`, which uses the exhaustive search algorithm by computing the distance values from all points in X to find nearest neighbors.
`obj.IncludeTies` |  | A boolean flag indicating whether prediction includes all the neighbors whose distance values are equal to the k^th smallest distance. If `IncludeTies` is `true`, prediction includes all of these neighbors. Otherwise, prediction uses exactly k neighbors.
`obj.BucketSize` |  | Maximum number of data points in the leaf node of the Kd-tree, specified as positive integer value. This argument is meaningful only when `NSMethod` is `"kdtree"`.

See also: fitcknn, knnsearch, rangesearch, pdist2

Source Code: ClassificationKNN

### Method: `predict`

ClassificationKNN: label = predict (obj, XC)
ClassificationKNN: [label, score, cost] = predict (obj, XC)

Classify new data points into categories using the kNN algorithm from a k-Nearest Neighbor classification model.

`label = predict (obj, XC)` returns the matrix of labels predicted for the corresponding instances in XC, using the predictor data in `obj.X` and corresponding labels, `obj.Y`, stored in the k-Nearest Neighbor classification model, obj.

XC must be an M×P numeric matrix with the same number of features P as the corresponding predictors of the kNN model in obj.

`[label, score, cost] = predict (obj, XC)` also returns score, which contains the predicted class scores or posterior probabilities for each instance of the corresponding unique classes, and cost, which is a matrix containing the expected cost of the classifications.

See also: fitcknn, ClassificationKNN

### Example: 1

     ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
     ## Evaluate some model predictions on new data.

     load fisheriris
     x = meas;
     y = species;
     xc = [min(x); mean(x); max(x)];
     obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
     [label, score, cost] = predict (obj, xc)

    label =
    {
      [1,1] = versicolor
      [2,1] = versicolor
      [3,1] = virginica
    }

    score =

       0.4000   0.6000        0
            0   1.0000        0
            0        0   1.0000

    cost =

       0.6000   0.4000   1.0000
       1.0000        0   1.0000
       1.0000   1.0000        0

### Example: 2

     ## Train a k-nearest neighbor classifier for k = 10
     ## and plot the decision boundaries.

     load fisheriris
     idx = ! strcmp (species, "setosa");
     X = meas(idx,3:4);
     Y = cast (strcmpi (species(idx), "virginica"), "double");
     obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
     x1 = [min(X(:,1)):0.03:max(X(:,1))];
     x2 = [min(X(:,2)):0.02:max(X(:,2))];
     [x1G, x2G] = meshgrid (x1, x2);
     XGrid = [x1G(:), x2G(:)];
     pred = predict (obj, XGrid);
     gidx = logical (str2num (cell2mat (pred)));

     figure
     scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
     hold on
     scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
     plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
     xlabel ("Petal length (cm)");
     ylabel ("Petal width (cm)");
     title ("5-Nearest Neighbor Classifier Decision Boundary");
     legend ({"Versicolor Region", "Virginica Region", ...
             "Sampled Versicolor", "Sampled Virginica"}, ...
             "location", "northwest")
     axis tight
     hold off

    obj =

      ClassificationKNN object with properties:

                BreakTies: smallest
               BucketSize: [1x1 double]
               ClassNames: [2x1 cell]
                     Cost: [2x2 double]
            DistParameter: [0x0 double]
                 Distance: euclidean
           DistanceWeight: [1x1 function_handle]
              IncludeTies: 0
                       Mu: [1x2 double]
                 NSMethod: exhaustive
             NumNeighbors: [1x1 double]
          NumObservations: [1x1 double]
            NumPredictors: [1x1 double]
           PredictorNames: [1x2 cell]
                    Prior: [2x1 double]
             ResponseName: Y
                 RowsUsed: [100x1 double]
                    Sigma: [1x2 double]
              Standardize: 1
                        X: [100x2 double]
                        Y: [100x1 double]