margin

Classification margins for discriminant analysis classifier

Syntax

m = margin(mdl,X,Y)

Description

m = margin(mdl,X,Y) returns the classification margins for the matrix of predictors X and class labels Y. For the definition, see Margin.

Examples

Classification Margin

Compute the classification margin for the Fisher iris data.

Compute the classification margin for the Fisher iris data, trained on its first two columns of data, and view the last 10 entries:

load fisheriris
X = meas(:,1:2);
obj = fitcdiscr(X,species);
M = margin(obj,X,species);
M(end-10:end)

ans =
    0.6551
    0.4838
    0.6551
   -0.5127
    0.5659
    0.4611
    0.4949
    0.1024
    0.2787
   -0.1439
   -0.4444

Compute the classification margin for the Fisher iris data, trained on its first two columns of data, and view the last 10 entries:

obj = fitcdiscr(meas,species);
M = margin(obj,meas,species);
M(end-10:end)

ans =
    0.9983
    1.0000
    0.9991
    0.9978
    1.0000
    1.0000
    0.9999
    0.9882
    0.9937
    1.0000
    0.9649

Input Arguments

mdl — Trained discriminant analysis classifier

ClassificationDiscriminant model object | CompactClassificationDiscriminant model object

Trained discriminant analysis classifier, specified as a ClassificationDiscriminant or CompactClassificationDiscriminant model object trained with fitcdiscr.

X — Predictor data

matrix

Predictor data to classify, specified as a matrix. Each row of the matrix represents an observation, and each column represents a predictor. The number of columns in X must equal the number of predictors in mdl.

Y — Class labels

same data type as in mdl

Class labels, specified with the same data type as data in mdl. The number of elements of Y must equal the number of rows of X.

Output Arguments

m — Classification margin

numeric column vector of length size(X,1)

Classification margin, returned as a numeric column vector of length size(X,1). Each entry in m represents the margin for the corresponding rows of X and (true class) Y, computed using mdl.

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 (discriminant analysis)

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.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. For more information, see Tall Arrays.

Version History

Introduced in R2011b