Documentation

# edge

Classification edge

## Syntax

```E = edge(obj,X,Y) E = edge(obj,X,Y,Name,Value) ```

## Description

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

`E = edge(obj,X,Y,Name,Value)` computes the edge with additional options specified by one or more `Name,Value` pair arguments.

## Input Arguments

 `obj` Discriminant analysis classifier of class `ClassificationDiscriminant` or `CompactClassificationDiscriminant`, typically constructed with `fitcdiscr`. `X` Matrix where each row represents an observation, and each column represents a predictor. The number of columns in `X` must equal the number of predictors in `obj`. `Y` Class labels, with the same data type as exists in `obj`. The number of elements of `Y` must equal the number of rows of `X`.

### Name-Value Pair Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

 `'weights'` Observation weights, a numeric vector of length `size(X,1)`. If you supply weights, `edge` computes the weighted classification edge. Default: `ones(size(X,1),1)`

## Output Arguments

 `E` Edge, a scalar representing the weighted average value of the margin.

## Examples

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

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

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