resubMargin
Resubstitution classification margins for discriminant analysis classifier
Syntax
Description
Examples
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.
load fisheriris
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
Input Arguments
mdl
— Trained discriminant analysis classifier
ClassificationDiscriminant
model object
Trained discriminant analysis classifier, specified as a ClassificationDiscriminant
model object. To create a discriminant analysis
classifier, use fitcdiscr
.
Output Arguments
M
— Classification margins
numeric column vector of length size(mdl.X,1)
Classification margins, returned as a numeric column vector of length
size(mdl.X,1)
.
More About
Margin
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.
Score
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 History
Introduced in R2011bR2023b: 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.
See Also
Classes
Functions
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