# CompactClassificationEnsemble

Compact classification ensemble

## Description

Compact version of a classification ensemble. The compact version does not include the data for training the classification ensemble. Therefore, you cannot perform some tasks with a compact classification ensemble, such as cross validation. Use a compact classification ensemble for making predictions (classifications) of new data.

## Creation

### Syntax

### Description

### Input Arguments

`fullEns`

— Classification ensemble object

output of `fitcensemble`

Classification ensemble object, specified as the output of `fitcensemble`

.

## Properties

`CategoricalPredictors`

— Indices of categorical predictors

vector of positive integers | `[]`

This property is read-only.

Categorical predictor
indices, specified as a vector of positive integers. `CategoricalPredictors`

contains index values indicating that the corresponding predictors are categorical. The index
values are between 1 and `p`

, where `p`

is the number of
predictors used to train the model. If none of the predictors are categorical, then this
property is empty (`[]`

).

**Data Types: **`single`

| `double`

`ClassNames`

— List of elements in `Y`

with duplicates removed

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

This property is read-only.

List of the elements in `Y`

with duplicates removed, returned as a
categorical array, cell array of character vectors, character array, logical vector, or
a numeric vector. `ClassNames`

has the same data type as the data in
the argument `Y`

. (The software treats string arrays as cell arrays of character
vectors.)

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `categorical`

`CombineWeights`

— How the ensemble combines weak learner weights

`'WeightedAverage'`

| `'WeightedSum'`

This property is read-only.

How the ensemble combines weak learner weights, returned as either
`'WeightedAverage'`

or `'WeightedSum'`

.

**Data Types: **`char`

`Cost`

— Cost of classifying a point into class `j`

when its true class is `i`

square matrix

Cost of classifying a point into class `j`

when its true class is
`i`

, returned as a square matrix. The rows of
`Cost`

correspond to the true class and the columns correspond to
the predicted class. The order of the rows and columns of `Cost`

corresponds to the order of the classes in `ClassNames`

. The number
of rows and columns in `Cost`

is the number of unique classes in the
response.

**Data Types: **`double`

`ExpandedPredictorNames`

— Expanded predictor names

cell array of character vectors

This property is read-only.

Expanded predictor names, returned as a cell array of character vectors.

If the model uses encoding for categorical variables, then
`ExpandedPredictorNames`

includes the names that describe the
expanded variables. Otherwise, `ExpandedPredictorNames`

is the same as
`PredictorNames`

.

**Data Types: **`cell`

`NumTrained`

— Number of trained weak learners

positive integer

This property is read-only.

Number of trained weak learners in the ensemble, returned as a positive integer.

**Data Types: **`double`

`PredictorNames`

— Predictor names

cell array of character vectors

This property is read-only.

Predictor names, specified as a cell array of character vectors. The order of the
entries in `PredictorNames`

is the same as in the training data.

**Data Types: **`cell`

`Prior`

— Prior probabilities for each class

`m`

-element vector

Prior probabilities for each class, returned as an `m`

-element
vector, where `m`

is the number of unique classes in the response. The
order of the elements of `Prior`

corresponds to the order of the
classes in `ClassNames`

.

**Data Types: **`double`

`ResponseName`

— Name of the response variable

character vector

This property is read-only.

Name of the response variable, returned as a character vector.

**Data Types: **`char`

`ScoreTransform`

— Function for transforming scores

function handle | name of a built-in transformation function | `'none'`

Function for transforming scores, specified as a function handle or the name of a built-in transformation function. `'none'`

means no transformation; equivalently, `'none'`

means `@(x)x`

. For a list of built-in transformation functions and the syntax of custom transformation functions, see `fitctree`

.

Add or change a `ScoreTransform`

function using dot notation:

ctree.ScoreTransform = 'function' % or ctree.ScoreTransform = @function

**Data Types: **`char`

| `string`

| `function_handle`

`Trained`

— Trained classification models

cell vector

Trained classification models, returned as a cell vector. The entries of the cell vector contain the corresponding compact classification models.

**Data Types: **`cell`

`TrainedWeights`

— Trained weak learner weights

numeric vector

This property is read-only.

Trained weights for the weak learners in the ensemble, returned as a numeric vector.
`TrainedWeights`

has `T`

elements, where
`T`

is the number of weak learners in
`learners`

. The ensemble computes predicted response by aggregating
weighted predictions from its learners.

**Data Types: **`double`

`UsePredForLearner`

— Indicator that learner `j`

uses predictor `i`

logical matrix

Indicator that learner `j`

uses predictor `i`

,
returned as a logical matrix of size
`P`

-by-`NumTrained`

, where `P`

is
the number of predictors (columns) in the training data.
`UsePredForLearner(i,j)`

is `true`

when learner
`j`

uses predictor `i`

, and is
`false`

otherwise. For each learner, the predictors have the same
order as the columns in the training data.

If the ensemble is not of type `Subspace`

, all entries in
`UsePredForLearner`

are `true`

.

**Data Types: **`logical`

## Object Functions

`compareHoldout` | Compare accuracies of two classification models using new data |

`edge` | Classification edge for classification ensemble model |

`gather` | Gather properties of Statistics and Machine Learning Toolbox object from GPU |

`lime` | Local interpretable model-agnostic explanations (LIME) |

`loss` | Classification loss for classification ensemble model |

`margin` | Classification margins for classification ensemble model |

`partialDependence` | Compute partial dependence |

`plotPartialDependence` | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |

`predict` | Predict labels using classification ensemble model |

`predictorImportance` | Estimates of predictor importance for classification ensemble of decision trees |

`removeLearners` | Remove members of compact classification ensemble |

`shapley` | Shapley values |

## Examples

### Reduce Size of Classification Ensemble

Create a compact classification ensemble for efficiently making predictions on new data.

Load the `ionosphere`

data set.

`load ionosphere`

Train a boosted ensemble of 100 classification trees using all measurements and the `AdaBoostM1`

method.

`Mdl = fitcensemble(X,Y,Method="AdaBoostM1")`

Mdl = ClassificationEnsemble ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'b' 'g'} ScoreTransform: 'none' NumObservations: 351 NumTrained: 100 Method: 'AdaBoostM1' LearnerNames: {'Tree'} ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.' FitInfo: [100x1 double] FitInfoDescription: {2x1 cell}

`Mdl`

is a `ClassificationEnsemble`

model object that contains the training data, among other things.

Create a compact version of `Mdl`

.

CMdl = compact(Mdl)

CMdl = CompactClassificationEnsemble ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'b' 'g'} ScoreTransform: 'none' NumTrained: 100

`CMdl`

is a `CompactClassificationEnsemble`

model object. `CMdl`

is almost the same as `Mdl`

. One exception is that `CMdl`

does not store the training data.

Compare the amounts of space consumed by `Mdl`

and `CMdl`

.

mdlInfo = whos("Mdl"); cMdlInfo = whos("CMdl"); [mdlInfo.bytes cMdlInfo.bytes]

`ans = `*1×2*
895597 648755

`Mdl`

consumes more space than `CMdl`

.

`CMdl.Trained`

stores the trained classification trees (`CompactClassificationTree`

model objects) that compose `Mdl`

.

Display a graph of the first tree in the compact ensemble.

`view(CMdl.Trained{1},Mode="graph");`

By default, `fitcensemble`

grows shallow trees for boosted ensembles of trees.

Predict the label of the mean of `X`

using the compact ensemble.

predMeanX = predict(CMdl,mean(X))

`predMeanX = `*1x1 cell array*
{'g'}

## Tips

For an ensemble of classification trees, the `Trained`

property
of `ens`

stores an `ens.NumTrained`

-by-1
cell vector of compact classification models. For a textual or graphical
display of tree * t* in the cell vector, enter:

`view(ens.Trained{`

for ensembles aggregated using LogitBoost or GentleBoost.}.CompactRegressionLearner)`t`

`view(ens.Trained{`

for all other aggregation methods.})`t`

## Extended Capabilities

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

The

`predict`

function supports code generation.To integrate the prediction of an ensemble into Simulink

^{®}, you can use the ClassificationEnsemble Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^{®}Function block with the`predict`

function.When you train an ensemble by using

`fitcensemble`

, the following restrictions apply.The value of the

`ScoreTransform`

name-value argument cannot be an anonymous function.Code generation limitations for the weak learners used in the ensemble also apply to the ensemble.

For decision tree weak learners, you cannot use surrogate splits; that is, the value of the

`Surrogate`

name-value argument must be`'off'`

.For

*k*-nearest neighbor weak learners, the value of the`Distance`

name-value argument cannot be a custom distance function. The value of the`DistanceWeight`

name-value argument can be a custom distance weight function, but it cannot be an anonymous function.

For fixed-point code generation, the following additional restrictions apply.

When you train an ensemble by using

`fitcensemble`

, you must train an ensemble using tree learners, and the`ScoreTransform`

value cannot be`'invlogit'`

.Categorical predictors (

`logical`

,`categorical`

,`char`

,`string`

, or`cell`

) are not supported. You cannot use the`CategoricalPredictors`

name-value argument. To include categorical predictors in a model, preprocess them by using`dummyvar`

before fitting the model.Class labels with the

`categorical`

data type are not supported. Both the class label value in the training data (`Tbl`

or`Y`

) and the value of the`ClassNames`

name-value argument cannot be an array with the`categorical`

data type.

For more information, see Introduction to Code Generation.

### GPU Arrays

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

Usage notes and limitations:

The following object functions fully support GPU arrays:

The following object functions offer limited support for GPU arrays:

The object functions execute on a GPU if at least one of the following applies:

The model was fitted with GPU arrays.

The predictor data that you pass to the object function is a GPU array.

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

## Version History

**Introduced in R2011a**

### R2022a: `Cost`

property stores the user-specified cost matrix

Starting in R2022a, the `Cost`

property stores the user-specified cost
matrix, so that you can compute the observed misclassification cost using the specified cost
value. The software stores normalized prior probabilities (`Prior`

)
that do not reflect the penalties described in the cost matrix. To compute the observed
misclassification cost, specify the `LossFun`

name-value argument as
`"classifcost"`

when you call the `loss`

function.

Note that model training has not changed and, therefore, the decision boundaries between classes have not changed.

For training, the fitting function updates the specified prior probabilities by
incorporating the penalties described in the specified cost matrix, and then normalizes the
prior probabilities and observation weights. This behavior has not changed. In previous
releases, the software stored the default cost matrix in the `Cost`

property and stored the prior probabilities used for training in the
`Prior`

property. Starting in R2022a, the software stores the
user-specified cost matrix without modification, and stores normalized prior probabilities that do
not reflect the cost penalties. For more details, see Misclassification Cost Matrix, Prior Probabilities, and Observation Weights.

Some object functions use the `Cost`

and `Prior`

properties:

The

`loss`

function uses the cost matrix stored in the`Cost`

property if you specify the`LossFun`

name-value argument as`"classifcost"`

or`"mincost"`

.The

`loss`

and`edge`

functions use the prior probabilities stored in the`Prior`

property to normalize the observation weights of the input data.

If you specify a nondefault cost matrix when you train a classification model, the object functions return a different value compared to previous releases.

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

`fitcensemble`

| `ClassificationEnsemble`

| `predict`

| `compact`

| `fitctree`

| `view`

| `compareHoldout`

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