# edge

Classification edge for classification tree model

## Description

returns the classification
edge
`E`

= edge(`tree`

,`Tbl`

,`ResponseVarName`

)`E`

for the trained classification tree model `tree`

using the predictor data in table `Tbl`

and the class labels in
`Tbl.ResponseVarName`

. The classification edge is a numeric scalar value
that represents the weighted average value of the classification
margin.

computes the edge using the observation weights specified in `E`

= edge(___,Weights=`weights`

)`weights`

in
addition to any of the input argument combinations in the previous syntaxes.

## Examples

### Compute Classification Margin and Edge

Compute the classification margin and edge 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);
tree = fitctree(X,species);
E = edge(tree,X,species)
E =
0.6299
M = margin(tree,X,species);
M(end-10:end)
```

ans = 0.1111 0.1111 0.1111 -0.2857 0.6364 0.6364 0.1111 0.7500 1.0000 0.6364 0.2000

The classification tree trained on all the data is better.

tree = fitctree(meas,species); E = edge(tree,meas,species) E = 0.9384 M = margin(tree,meas,species); M(end-10:end)

ans = 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565 0.9565

## Input Arguments

`tree`

— Trained classification tree

`ClassificationTree`

model object | `CompactClassificationTree`

model object

Trained classification tree, specified as a `ClassificationTree`

model object trained with `fitctree`

, or a `CompactClassificationTree`

model object
created with `compact`

.

`X`

— Predictor data

numeric matrix

`Tbl`

— Sample data

table

Sample data, specified as a table. Each row of `Tbl`

corresponds to
one observation, and each column corresponds to one predictor variable. Optionally,
`Tbl`

can contain additional columns for the response variable
and observation weights. `Tbl`

must contain all the predictors used
to train `tree`

. Multicolumn variables and cell arrays other than
cell arrays of character vectors are not allowed.

If `Tbl`

contains the response variable used to train
`tree`

, then you do not need to specify
`ResponseVarName`

or `Y`

.

If you train `tree`

using sample data contained in a table, then
the input data for `edge`

must also be in a table.

**Data Types: **`table`

`ResponseVarName`

— Response variable name

name of variable in `Tbl`

Response variable name, specified as the name of a variable in `Tbl`

. If
`Tbl`

contains the response variable used to train
`tree`

, then you do not need to specify
`ResponseVarName`

.

You must specify `ResponseVarName`

as a character vector or string scalar.
For example, if the response variable is stored as `Tbl.Response`

, then
specify it as `"Response"`

. Otherwise, the software treats all columns
of `Tbl`

, including `Tbl.Response`

, as
predictors.

The response variable must be a categorical, character, or string array, a logical or numeric vector, or a cell array of character vectors. If the response variable is a character array, then each element must correspond to one row of the array.

**Data Types: **`char`

| `string`

`Y`

— Class labels

categorical array | character array | string array | logical vector | numeric vector | cell array of character vectors

Class labels, specified as a categorical, character, or string array, a logical or numeric
vector, or a cell array of character vectors. `Y`

must be
of the same type as the class labels used to train
`tree`

, and its number of elements must equal the number
of rows of `X`

.

**Data Types: **`categorical`

| `char`

| `string`

| `logical`

| `single`

| `double`

| `cell`

`weights`

— Observation weights

`ones(size(X,1),1)`

(default) | numeric vector | name of variable in `Tbl`

Observation weights, specified as a numeric vector or the name of a variable in
`Tbl`

.

If you specify `weights`

as a numeric vector, then the size of
`weights`

must be equal to the number of rows in
`X`

or `Tbl`

.

If you specify `weights`

as the name of a variable in
`Tbl`

, then the name must be a character vector or string scalar.
For example, if the weights are stored as `Tbl.W`

, then specify
`weights`

as `"W"`

. Otherwise, the software treats
all columns of `Tbl`

, including `Tbl.W`

, as
predictors.

When you supply weights, `edge`

computes the weighted classification edge. The
software weighs the observations in each row of `X`

or
`Tbl`

with the corresponding weight in
`weights`

.

**Data Types: **`single`

| `double`

| `char`

| `string`

## 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. Margin is a column vector with the same number of rows as the
matrix `X`

.

### Score (tree)

For trees, the *score* of a classification
of a leaf node is the posterior probability of the classification
at that node. The posterior probability of the classification at a
node is the number of training sequences that lead to that node with
the classification, divided by the number of training sequences that
lead to that node.

For an example, see Posterior Probability Definition for Classification Tree.

### Edge

The *edge* is the weighted mean value of the classification margin.
The weights are the class probabilities in
`tree`

`.Prior`

. If you supply
`weights`

, those weights are normalized to sum to the prior probabilities
in the respective classes, and are then used to compute the weighted average.

## Extended Capabilities

### Tall Arrays

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

The
`edge`

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

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

The

`edge`

function does not support decision tree models trained with surrogate splits.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2011a**

## See Also

`margin`

| `loss`

| `predict`

| `fitctree`

| `ClassificationTree`

| `CompactClassificationTree`

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