Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

`E = edge(tree,TBL,ResponseVarName)`

`E = edge(tree,X,Y)`

`E = edge(___,Name,Value)`

returns
the classification edge for `E`

= edge(`tree`

,`TBL`

,`ResponseVarName`

)`tree`

with data `TBL`

and
classification `TBL.ResponseVarName`

.

computes
the edge with additional options specified by one or more `E`

= edge(___,`Name,Value`

)`Name,Value`

pair
arguments, using any of the previous syntaxes. For example, you can
specify observation weights.

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`

.

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 example, consider classifying a predictor `X`

as `true`

when `X`

< `0.15`

or `X`

> `0.95`

, and `X`

is
false otherwise.

Generate 100 random points and classify them:

rng(0,'twister') % for reproducibility X = rand(100,1); Y = (abs(X - .55) > .4); tree = fitctree(X,Y); view(tree,'Mode','Graph')

Prune the tree:

tree1 = prune(tree,'Level',1); view(tree1,'Mode','Graph')

The pruned tree correctly classifies observations that are less
than 0.15 as `true`

. It also correctly classifies
observations from .15 to .94 as `false`

. However,
it incorrectly classifies observations that are greater than .94 as `false`

.
Therefore, the score for observations that are greater than .15 should
be about .05/.85=.06 for `true`

, and about .8/.85=.94
for `false`

.

Compute the prediction scores for the first 10 rows of `X`

:

[~,score] = predict(tree1,X(1:10)); [score X(1:10,:)]

ans = 0.9059 0.0941 0.8147 0.9059 0.0941 0.9058 0 1.0000 0.1270 0.9059 0.0941 0.9134 0.9059 0.0941 0.6324 0 1.0000 0.0975 0.9059 0.0941 0.2785 0.9059 0.0941 0.5469 0.9059 0.0941 0.9575 0.9059 0.0941 0.9649

Indeed, every value of `X`

(the right-most
column) that is less than 0.15 has associated scores (the left and
center columns) of `0`

and `1`

,
while the other values of `X`

have associated scores
of `0.91`

and `0.09`

. The difference
(score `0.09`

instead of the expected `.06`

)
is due to a statistical fluctuation: there are `8`

observations
in `X`

in the range `(.95,1)`

instead
of the expected `5`

observations.

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

`.Prior`

. If you supply
weights in the `weights`

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

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

Was this topic helpful?