A numeric matrix containing the unstandardized predictor data. Each column of X represents one predictor (variable), and each row represents one observation. This property is read-only.

Specified as a logical or numeric column vector, or as a character array or a cell array of character vectors with the same number of rows as the predictor data. Each row in Y is the observed class label for the corresponding row in X. This property is read-only.

A positive integer value specifying the number of observations in the training dataset used for training the ClassificationKNN model. This property is read-only.

A logical column vector with the same length as the observations in the original predictor data X specifying which rows have been used for fitting the ClassificationKNN model. This property is read-only.

A positive integer value specifying the number of predictors in the training dataset used for training the ClassificationKNN model. This property is read-only.

A cell array of character vectors specifying the names of the predictor variables. The names are in the order in which the appear in the training dataset. This property is read-only.

A character vector specifying the name of the response variable Y. This property is read-only.

An array of unique values of the response variable Y, which has the same data types as the data in Y. This property is read-only. ClassNames can have any of the following datatypes:

• Cell array of character vectors
• Character array
• Logical vector
• Numeric vector

A square matrix specifying the cost of misclassification of a point. 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.

Add or change the Cost property using dot notation as in:

• obj.Cost = costMatrix

A numeric vector specifying the prior probabilities for each class. The order of the elements in Prior corresponds to the order of the classes in ClassNames.

Add or change the Prior property using dot notation as in:

• obj.Prior = priorVector

Specified as a function handle for transforming the classification scores. Add or change the ScoreTransform property using dot notation as in:

• obj.ScoreTransform = 'function_name'
• obj.ScoreTransform = @function_handle

When specified as a character vector, it can be any of the following built-in functions. Nevertherless, the ScoreTransform property always stores their function handle equivalent.

A logical scalar specifying whether the predictor data in X have been standardized prior to training. This property is read-only.

A numeric vector of the same length as the columns in X with the standard deviations corresponding to each predictor. If the predictor variables have not been standardized, then "obj.Sigma" is empty. This property is read-only.

A numeric vector of the same length as the columns in X with the mean values corresponding to each predictor. If the predictor variables have not been standardized, then "obj.Sigma" is empty. This property is read-only.

A character vector specifying the tie-breaking algorithm used by the predict method, when multiple classes have the same smallest cost. It can be one of the following:

• "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.
• "random", which randomly picks one class among the tied groups.

The tie-breaking algorithm is only used when IncludeTies is false. Change the BreakTies property using dot notation as in:

• obj.BreakTies = algorithm

A positive integer value specifyingNumber of nearest neighbors in X used to classify each point during prediction. Change the NumNeighbors property using dot notation as in:

• obj.NumNeighbors = newNumNeighbors

A character vector specifying the distance metric used by the neighbor-searcher method. See the available distance metrics in knnseaarch for more info. Change the Distance property using dot notation as in:

• obj.Distance = newDistance

A character vector or a function handle specifying the distance weighting function, which can be any of the following values:

• "equal", which corresponds to @(d) d.
• "inverse", which corresponds to @(d) 1/d.
• "squaredinverse", which corresponds to @(d) 1/d.^2.
• @fcn, which is a function handle that accepts a matrix of nonnegative distances, and returns a matrix the same size containing nonnegative distance weights.

Change the DistanceWeight property using dot notation as in:

• obj.DistanceWeight = newDistanceWeight

A positive definite covariance matrix, a positive scalar, or a vector of positive scale values specifying the parameter for the corresponding distance metric as shown below:

• "mahalanobis" accepts a positive definite covariance matrix.
• "minkowski" accepts a positive scalar as the Minkowski distance exponent.
• "seuclidean" accepts a vector of positive scale values of equal length as the number of predictors in X.

For any other distance metric, DistParameter is empty ([]). Change the DistParameter property using dot notation as in:

• obj.DistParameter = distParam

A character vector 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.

Change the NSMethod property using dot notation as in:

• obj.NSMethod = newNSMethod

A logical scalar specifying 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.

Change the IncludeTies property using dot notation as in:

• obj.IncludeTies = flag

A positive integer scalar specifying the maximum number of data points in the leaf node of the Kd-tree. BucketSize only applies when the NSMethod property is "kdtree".

Change the BucketSize property using dot notation as in:

• obj.BucketSize = maxnum

statistics: obj = ClassificationKNN (X, Y)

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

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 $N\times P$ 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 $N\times 1$ 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 the following name, value paired input arguments:

See also: fitcknn, knnsearch, rangesearch, pdist2

ClassificationKNN: labels = predict (obj, XC)

ClassificationKNN: [labels, scores, cost] = predict (obj, XC)

labels = 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.

• obj must be a ClassificationKNN class object.
• XC must be an $M\times P$ numeric matrix with the same number of features $P$ as the corresponding predictors of the SVM model in obj.

[labels, scores, cost] = predict (obj, XC) also returns scores, 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. By default, scores returns the posterior probabilities for KNN models, unless a specific ScoreTransform function has been specified. See fitcknn for more info.

Note! predict is explicitly using "exhaustive" as the nearest search method due to the very slow implementation of "kdtree" in the knnsearch function.

See also: fitcknn, ClassificationKNN, knnsearch

ClassificationKNN: L = loss (obj, X, Y)

ClassificationKNN: L = loss (…, name, value)

L = loss (obj, X, Y) computes the loss, L, using the default loss function 'mincost'.

• obj is a ClassificationKNN object trained on X and Y.
• X must be a $N\times P$ numeric matrix of input data where rows correspond to observations and columns correspond to features or variables.
• Y is $N\times 1$ matrix or cell matrix containing the class labels of corresponding predictor data in X. Y must have same numbers of Rows as X.

L = loss (…, name, value) allows additional options specified by name-value pairs:

See also: fitcknn, ClassificationKNN

ClassificationKNN: m = margin (obj, X, Y)

• obj is a ClassificationKNN object trained on X and Y.
• X must be a $N\times P$ numeric matrix of input data where rows correspond to observations and columns correspond to features or variables.
• Y is $N\times 1$ matrix or cell matrix containing the class labels of corresponding predictor data in X. Y must have same numbers of Rows as X.

The classification margin for each observation is the difference between the classification score for the true class and the maximal classification score for the false classes.

See also: fitcknn, ClassificationKNN

ClassificationKNN: [pd, x, y] = partialDependence (obj, Vars, Labels)

ClassificationKNN: [pd, x, y] = partialDependence (…, Data)

ClassificationKNN: [pd, x, y] = partialDependence (…, name, value)

[pd, x, y] = partialDependence (obj, Vars, Labels) computes the partial dependence of the classification scores on the variables Vars for the specified class Labels.

• obj is a trained ClassificationKNN object.
• Vars is a vector of positive integers, character vector, string array, or cell array of character vectors representing predictor variables (it can be indices of predictor variables in obj.X).
• Labels is a character vector, logical vector, numeric vector, or cell array of character vectors representing class labels. (column vector)

[pd, x, y] = partialDependence (…, Data) specifies new predictor data to use for computing the partial dependence.

[pd, x, y] = partialDependence (…, name, value) allows additional options specified by name-value pairs:

Return Values

• pd: Partial dependence values.
• x: Query points for the first predictor variable in Vars.
• y: Query points for the second predictor variable in Vars (if applicable).

See also: fitcknn, ClassificationKNN

ClassificationKNN: CVMdl = crossval (obj)

ClassificationKNN: CVMdl = crossval (…, Name, Value)

CVMdl = crossval (obj) returns a cross-validated model object, CVMdl, from a trained model, obj, using 10-fold cross-validation by default.

CVMdl = crossval (obj, name, value) specifies additional name-value pair arguments to customize the cross-validation process.

See also: fitcknn, ClassificationKNN, cvpartition, ClassificationPartitionedModel

ClassificationKNN: savemodel (obj, filename)

savemodel (obj, filename) saves each property of a ClassificationKNN object into an Octave binary file, the name of which is specified in filename, along with an extra variable, which defines the type classification object these variables constitute. Use loadmodel in order to load a classification object into Octave’s workspace.

See also: loadmodel, fitcknn, ClassificationKNN