resubEdge
Resubstitution classification edge for classification ensemble model
Description
returns the resubstitution Classification Edge
(edge
= resubEdge(ens
)edge
) for the trained classification ensemble
model ens
using the training data stored in
ens.X
and the corresponding true class
labels stored in ens.Y
. The classification edge
is the Classification Margin averaged over the entire
data set. edge
can be a scalar or vector,
depending on the setting of the Mode
name-value
argument.
specifies additional options using one or more name-value arguments.
For example, you can specify the indices of the weak learners to use
for calculating the loss, select the aggregation level for the
output, and perform computations in parallel.edge
= resubEdge(ens
,Name=Value
)
Examples
Find Classification Edge by Resubstitution of Training Data
Find the resubstitution edge for an ensemble that classifies the Fisher iris data.
Load the sample data set.
load fisheriris
Train an ensemble of 100 boosted classification trees using AdaBoostM2.
t = templateTree(MaxNumSplits=1); % Weak learner template tree object ens = fitcensemble(meas,species,"Method","AdaBoostM2","Learners",t);
Find the resubstitution edge.
edge = resubEdge(ens)
edge = 3.2486
Input Arguments
ens
— Classification ensemble model
ClassificationEnsemble
model object | ClassificationBaggedEnsemble
model object
Classification ensemble model, specified as a ClassificationEnsemble
or ClassificationBaggedEnsemble
model object trained with fitcensemble
.
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: resubEdge(ens,Learners=[1 2 3
5],UseParallel=true)
specifies to use the first,
second, third, and fifth learners in the ensemble in
resubEdge
, and to perform
computations in parallel.
Learners
— Indices of weak learners
[1:ens.NumTrained]
(default) | vector of positive integers
Indices of the weak learners in the ensemble to use with
resubEdge
, specified as a
vector of positive integers in the range
[1:ens.NumTrained
]. By default,
the function uses all learners.
Example: Learners=[1 2 4]
Data Types: single
| double
Mode
— Aggregation level for output
"ensemble"
(default) | "individual"
| "cumulative"
Aggregation level for the output, specified as "ensemble"
,
"individual"
, or "cumulative"
.
Value | Description |
---|---|
"ensemble" | The output is a scalar value, the loss for the entire ensemble. |
"individual" | The output is a vector with one element per trained learner. |
"cumulative" | The output is a vector in which element J is
obtained by using learners 1:J from the input
list of learners. |
Example: Mode="individual"
Data Types: char
| string
UseParallel
— Flag to run in parallel
false
or 0
(default) | true
or 1
Flag to run in parallel, specified as a numeric or logical
1
(true
) or 0
(false
). If you specify UseParallel=true
, the
resubEdge
function executes for
-loop iterations by
using parfor
. The loop runs in parallel when you
have Parallel Computing Toolbox™.
Example: UseParallel=true
Data Types: logical
More About
Classification Edge
The classification edge is the weighted mean
value of the classification margin. The weights are the class
probabilities in
ens
.Prior
.
Classification 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 in the matrix
ens
.X
.
Score (ensemble)
For ensembles, a classification score represents the confidence of a classification into a class. The higher the score, the higher the confidence.
Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:
AdaBoostM1
scores range from –∞ to ∞.Bag
scores range from0
to1
.
Extended Capabilities
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
To run in parallel, set the UseParallel
name-value argument to
true
in the call to this function.
For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).
You cannot use UseParallel
with GPU arrays.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
You cannot use
UseParallel
with GPU arrays.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2011aR2023b: Observations with missing predictor values are used in resubstitution and cross-validation computations
Starting in R2023b, the following classification model object functions use observations with missing predictor values as part of resubstitution ("resub") and cross-validation ("kfold") computations for classification edges, losses, margins, and predictions.
In previous releases, the software omitted observations with missing predictor values from the resubstitution and cross-validation computations.
R2022a: resubEdge
returns a different value for a model with a nondefault cost matrix
If you specify a nondefault cost matrix when you train the input model object, the resubEdge
function returns a different value compared to previous releases.
The resubEdge
function uses the
observation weights stored in the W
property. The way the function uses the
W
property value has not changed. However, the property value stored in the input model object has changed for a
model with a nondefault cost matrix, so the function might return a different value.
For details about the property value changes, see Cost property stores the user-specified cost matrix.
If you want the software to handle the cost matrix, prior
probabilities, and observation weights in the same way as in previous releases, adjust the prior
probabilities and observation weights for the nondefault cost matrix, as described in Adjust Prior Probabilities and Observation Weights for Misclassification Cost Matrix. Then, when you train a
classification model, specify the adjusted prior probabilities and observation weights by using
the Prior
and Weights
name-value arguments, respectively,
and use the default cost matrix.
See Also
resubMargin
| resubLoss
| resubPredict
| resubEdge
| ClassificationEnsemble
| ClassificationBaggedEnsemble
| fitcensemble
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