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predict

Class: ClassificationKNN

Predict labels using k-nearest neighbor classification model

Syntax

label = predict(Mdl,X)
[label,score,cost] = predict(Mdl,X)

Description

label = predict(Mdl,X) returns a vector of predicted class label for the predictor data in the table or matrix X, based on the trained k-nearest neighbor classification model Mdl.

[label,score,cost] = predict(Mdl,X) also returns:

  • A matrix of classification scores (score) indicating the likelihood that a label comes from a particular class. For k-nearest neighbor, scores are posterior probabilities.

  • A matrix of expected classification cost (cost). For each observation in X, the predicted class label corresponds to the minimum expected classification costs among all classes.

Input Arguments

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k-nearest neighbor classification model, specified as a ClassificationKNN model object returned by fitcknn.

Predictor data to be classified, specified as a numeric matrix or table.

Each row of X corresponds to one observation, and each column corresponds to one variable.

  • For a numeric matrix:

    • The variables making up the columns of X must have the same order as the predictor variables that trained Mdl.

    • If you trained Mdl using a table (for example, Tbl), then X can be a numeric matrix if Tbl contains all numeric predictor variables. To treat all numeric predictors in Tbl as categorical during training (k-nearest neighbors requires homogeneous predictors), set CategoricalPredictors,'all' when you train using fitcknn. If Tbl contains heterogeneous predictors (for example, numeric and categorical data types) and X is a numeric matrix, then predict throws an error.

  • For a table:

    • predict does not support multi-column variables and cell arrays other than cell arrays of character vectors.

    • If you trained Mdl using a table (for example, Tbl), then all predictor variables in X must have the same variable names and data types as those that trained Mdl (stored in Mdl.PredictorNames). However, the column order of X does not need to correspond to the column order of Tbl. Tbl and X can contain additional variables (response variables, observation weights, etc.), but predict ignores them.

    • If you trained Mdl using a numeric matrix, then the predictor names in Mdl.PredictorNames and corresponding predictor variable names in X must be the same. To specify predictor names during training, see the PredictorNames name-value pair argument of fitcknn. All predictor variables in X must be numeric vectors. X can contain additional variables (response variables, observation weights, etc.), but predict ignores them.

If you set 'Standardize',true in fitcknn to train Mdl, then the software standardizes the columns of X using the corresponding means in Mdl.Mu and standard deviations in Mdl.Sigma.

Data Types: table | double | single

Output Arguments

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Predicted class labels for the observations (rows) in X, returned as a vector with length equal to the number of rows of X. The label is the class with minimal expected cost. See Predicted Class Label.

Predicted class scores or posterior probabilities, returned as a numeric matrix of size N-by-K. N is the number of observations (rows) in X, and K is the number of classes (in Mdl.ClassNames). score(i,j) is the posterior probability that observation i in X is of class j in Mdl.ClassNames. See Posterior Probability.

Expected costs, returned as a matrix of size N-by-K. N is the number of observations (rows) in X, and K is the number of classes (in Mdl.ClassNames). cost(i,j) is the cost of classifying row i of X as class j in Mdl.ClassNames. See Expected Cost.

Examples

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Construct a k-nearest neighbor classifier for Fisher's iris data, where k = 5. Evaluate some model predictions on new data.

Load the data.

load fisheriris
X = meas;
Y = species;

Construct a classifier for 5-nearest neighbors. It is good practice to standardize non-categorical predictor data.

mdl = fitcknn(X,Y,'NumNeighbors',5,'Standardize',1);

Predict the classifications for flowers with minimum, mean, and maximum characteristics.

Xnew = [min(X);mean(X);max(X)];
[label,score,cost] = predict(mdl,Xnew)
label =

  3×1 cell array

    'versicolor'
    'versicolor'
    '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

The classifications have binary values for the score and cost matrices, meaning all five nearest neighbors of each of the three points have identical classifications.

Definitions

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Extended Capabilities

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