Resubstitution classification margins for discriminant analysis classifier
Estimate Resubstitution Margins for Discriminant Analysis Classifiers
Find the margins for a discriminant analysis classifier for Fisher's iris data by resubstitution. Examine several entries.
Load Fisher's iris data set.
Train a discriminant analysis classifier.
Mdl = fitcdiscr(meas,species);
Compute the resubstitution margins, and display several of them.
m = resubMargin(Mdl); m(1:25:end)
ans = 6×1 1.0000 1.0000 0.9998 0.9998 1.0000 0.9946
M — Classification margins
numeric column vector of length
Classification margins, returned as 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.
The classification margin is a column vector with the same number
of rows as in the matrix
X. A high value of margin
indicates a more reliable prediction than a low value.
For discriminant analysis, the score of a classification is the posterior probability of the classification. For the definition of posterior probability in discriminant analysis, see Posterior Probability.
Version HistoryIntroduced in R2011b
R2023b: Observations with missing predictor values are used in resubstitution and cross-validation computations
Starting in R2023b, the following classification model object functions use observations with missing predictor values as part of resubstitution ("resub") and cross-validation ("kfold") computations for classification edges, losses, margins, and predictions.
In previous releases, the software omitted observations with missing predictor values from the resubstitution and cross-validation computations.