@ClassificationKNN/ClassificationKNN
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 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 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
smallest distance. If IncludeTies is true,
prediction includes all of these neighbors. Otherwise, prediction uses
exactly 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, @ClassificationKNN/predict, knnsearch
Source Code: @ClassificationKNN/ClassificationKNN