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resubMargin

Class: ClassificationKNN

Margin of k-nearest neighbor classifier by resubstitution

Syntax

m = resubMargin(mdl)

Description

m = resubMargin(mdl) returns the classification margins of the data used to train mdl. For the definition, see Margin.

Input Arguments

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mdl — Classifier modelclassifier model object

k-nearest neighbor classifier model, returned as a classifier model object.

Note that using the 'CrossVal', 'KFold', 'Holdout', 'Leaveout', or 'CVPartition' options results in a model of class ClassificationPartitionedModel. You cannot use a partitioned tree for prediction, so this kind of tree does not have a predict method.

Otherwise, mdl is of class ClassificationKNN, and you can use the predict method to make predictions.

Output Arguments

m

A numeric column vector of length size(mdl.X,1), where mdl.X is the training data for mdl. Each entry in m represents the margin for the corresponding row of mdl.X and (true class) mdl.Y.

Definitions

Margin

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

Margin is a column vector with the same number of rows as in the training data.

Score

The score of a classification is the posterior probability of the classification. The posterior probability is the number of neighbors that have that classification, divided by the number of neighbors. For a more detailed definition that includes weights and prior probabilities, see Posterior Probability.

Examples

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Resubstitution Margin Calculation

Construct a k-nearest neighbor classifier for the Fisher iris data, where k = 5.

Load the data.

load fisheriris
X = meas;
Y = species;

Construct a classifier for 5-nearest neighbors.

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

Examine some statistics of the resubstitution margin of the classifier.

m = resubMargin(mdl);
[max(m) min(m) mean(m)]
ans =

    1.0000   -0.6000    0.9253

The mean margin is over 0.9, indicating fairly high classification accuracy for resubstitution. For more reliable assessment of model accuracy, consider cross validation, such as kfoldLoss.

See Also

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