edge

Classification edge for discriminant analysis classifier

Syntax

E = edge(mdl,X,Y)

E = edge(mdl,X,Y,Weights=w)

Description

E = edge(mdl,X,Y) returns the classification Edge for mdl with data X and classification Y.

Note

If the predictor data X contains any missing values, the edge function might return NaN. For more details, see edge can return NaN for predictor data with missing values.

E = edge(mdl,X,Y,Weights=w) computes the weighted classification edge.

Examples

Classification Edge and Margin for Fisher Iris Data

Compute the classification edge and 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);
E = edge(obj,X,species)

E = 0.4980

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

ans = 11×1

    0.6551
    0.4838
    0.6551
   -0.5127
    0.5659
    0.4611
    0.4949
    0.1024
    0.2787
   -0.1439
      ⋮

The classifier trained on all the data is better.

obj = fitcdiscr(meas,species);
E = edge(obj,meas,species)

E = 0.9454

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

ans = 11×1

    0.9983
    1.0000
    0.9991
    0.9978
    1.0000
    1.0000
    0.9999
    0.9882
    0.9937
    1.0000
      ⋮

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.

w — Observation weights

ones(size(X,1),1) (default) | numeric vector of length size(X,1)

Observation weights, specified as a numeric vector of length size(X,1).

Output Arguments

E — Weighted average value of margin

numeric scalar

Weighted mean value of the classification margin, returned as a numeric scalar.

More About

Edge

The edge is the weighted mean value of the classification margin. The weights are class prior probabilities. If you supply additional weights, those weights are normalized to sum to the prior probabilities in the respective classes, and are then used to compute the weighted average.

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

R2022a: edge can return NaN for predictor data with missing values

The edge function no longer omits an observation with a NaN score when computing the weighted mean of the classification margins. Therefore, edge can now return NaN when the predictor data X contains any missing values. In most cases, if the test set observations do not contain missing predictors, the edge function does not return NaN.

This change improves the automatic selection of a classification model when you use fitcauto. Before this change, the software might select a model (expected to best classify new data) with few non-NaN predictors.

If edge in your code returns NaN, you can update your code to avoid this result. Remove or replace the missing values by using rmmissing or fillmissing, respectively.

The following table shows the classification models for which the edge object function might return NaN. For more details, see the Compatibility Considerations for each edge function.

Model Type	Full or Compact Model Object	edge Object Function
Discriminant analysis classification model	ClassificationDiscriminant, CompactClassificationDiscriminant	edge
Ensemble of learners for classification	ClassificationEnsemble, CompactClassificationEnsemble	edge
Gaussian kernel classification model	ClassificationKernel	edge
k-nearest neighbor classification model	ClassificationKNN	edge
Linear classification model	ClassificationLinear	edge
Neural network classification model	ClassificationNeuralNetwork, CompactClassificationNeuralNetwork	edge
Support vector machine (SVM) classification model	ClassificationSVM, CompactClassificationSVM	edge