Classification edge
E = edge(obj,X,Y)
E = edge(obj,X,Y,Name,Value)
returns the classification edge for E
= edge(obj
,X
,Y
)obj
with data
X
and classification Y
.
computes the edge with additional options specified by one or more
E
= edge(obj
,X
,Y
,Name,Value
)Name,Value
pair arguments.
|
Discriminant analysis classifier of class |
|
Matrix where each row represents an observation, and each column
represents a predictor. The number of columns in |
|
Class labels, with the same data type as exists in |
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
.
|
Observation weights, a numeric vector of length
Default: |
|
Edge, a scalar representing the weighted average value of the margin. |
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
ClassificationDiscriminant
| fitcdiscr
| loss
| margin
| predict