Margin of k-nearest neighbor classifier by resubstitution
m = resubMargin(mdl)
mdl— Classifier model
k-nearest neighbor classifier model, returned as a classifier model object.
Note that using the
'CVPartition' options results in a model of
You cannot use a partitioned tree for prediction, so this kind of
tree does not have a
A numeric column vector of length
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.
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.
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