# ClassificationEnsemble

Ensemble classifier

## Description

`ClassificationEnsemble`

combines a set of
trained weak learner models and data on which these learners were trained. It can
predict ensemble response for new data by aggregating predictions from its weak
learners. It stores data used for training, can compute resubstitution predictions, and
can resume training if desired.

## Creation

### Description

Create a classification ensemble object (`ens`

) using `fitcensemble`

.

## Properties

`BinEdges`

— Bin edges for numeric predictors

cell array of *p* numeric vectors

This property is read-only.

Bin edges for numeric predictors, specified as a cell array of *p* numeric vectors, where *p* is the number of predictors. Each vector includes the bin edges for a numeric predictor. The element in the cell array for a categorical predictor is empty because the software does not bin categorical predictors.

The software bins numeric predictors only if you specify the `'NumBins'`

name-value argument as a positive integer scalar when training a model with tree learners.
The `BinEdges`

property is empty if the `'NumBins'`

value is empty (default).

You can reproduce the binned predictor data `Xbinned`

by using the
`BinEdges`

property of the trained model
`mdl`

.

```
X = mdl.X; % Predictor data
Xbinned = zeros(size(X));
edges = mdl.BinEdges;
% Find indices of binned predictors.
idxNumeric = find(~cellfun(@isempty,edges));
if iscolumn(idxNumeric)
idxNumeric = idxNumeric';
end
for j = idxNumeric
x = X(:,j);
% Convert x to array if x is a table.
if istable(x)
x = table2array(x);
end
% Group x into bins by using the
````discretize`

function.
xbinned = discretize(x,[-inf; edges{j}; inf]);
Xbinned(:,j) = xbinned;
end

`Xbinned`

contains the bin indices, ranging from 1 to the number of bins, for numeric predictors.
`Xbinned`

values are 0 for categorical predictors. If
`X`

contains `NaN`

s, then the corresponding
`Xbinned`

values are `NaN`

s.
`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`

`FitInfo`

— Fit information

numeric array

Fit information, returned as a numeric array. The `FitInfoDescription`

property describes the content of this array.

**Data Types: **`double`

`FitInfoDescription`

— Description of information in `FitInfo`

character vector | cell array of character vectors

Description of the information in `FitInfo`

, returned as a character vector or cell array of character vectors.

**Data Types: **`char`

| `cell`

`HyperparameterOptimizationResults`

— Description of cross-validation optimization of hyperparameters

`BayesianOptimization`

object | table of hyperparameters and associated values

This property is read-only.

Description of the cross-validation optimization of hyperparameters, returned as a
`BayesianOptimization`

object or a table of
hyperparameters and associated values. Nonempty when the
`OptimizeHyperparameters`

name-value pair is nonempty at creation.
Value depends on the setting of the `HyperparameterOptimizationOptions`

name-value pair at creation:

`'bayesopt'`

(default) — Object of class`BayesianOptimization`

`'gridsearch'`

or`'randomsearch'`

— Table of hyperparameters used, observed objective function values (cross-validation loss), and rank of observations from lowest (best) to highest (worst)

`LearnerNames`

— Names of weak learners in ensemble

cell array of character vectors

This property is read-only.

Names of weak learners in ensemble, returned as a cell array of character vectors. The
name of each learner appears just once. For example, if you have an ensemble of 100
trees, `LearnerNames`

is `{'Tree'}`

.

**Data Types: **`cell`

`Method`

— Method that creates ensemble

character vector

Method that `fitcensemble`

uses to create the ensemble, returned as a character vector.

**Example: **`'AdaBoostM1'`

**Data Types: **`char`

`ModelParameters`

— Parameters used in training ensemble

`EnsembleParams`

object

Parameters used in training the ensemble, returned as an `EnsembleParams`

object. The properties of `ModelParameters`

include the type of ensemble, either `'classification'`

or `'regression'`

, the `Method`

used to create the ensemble, and other parameters, depending on the ensemble.

`NumObservations`

— Number of observations in the training data

positive integer

This property is read-only.

Number of observations in the training data, returned as a positive integer.
`NumObservations`

can be less than the number of rows of input data
when there are missing values in the input data or response data.

**Data Types: **`double`

`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`

`ReasonForTermination`

— Reason that `fitcensemble`

stopped adding weak learners to ensemble

character vector

Reason that `fitcensemble`

stopped adding weak learners to the ensemble, returned as a character vector.

**Example: **`'Terminated normally after completing the requested number of training cycles.'`

**Data Types: **`char`

`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`

`RowsUsed`

— Rows of the original predictor data `X`

used for fitting

logical vector

This property is read-only.

Rows of the original predictor data `X`

used for fitting, returned as an
`n`

-element logical vector, where `n`

is the
number of rows of `X`

. If the software uses all rows of
`X`

for constructing the object, then `RowsUsed`

is an empty array (`[]`

).

**Data Types: **`logical`

`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`

`W`

— Scaled weights in ensemble

numeric vector

This property is read-only.

Scaled weights in the ensemble, returned as a numeric vector. `W`

has length `n`

, the number of rows in the training data. The sum of the elements of `W`

is `1`

.

**Data Types: **`double`

`X`

— Predictor values

real matrix | table

This property is read-only.

Predictor values, returned as a real matrix or table. Each column of
`X`

represents one variable (predictor), and each row represents
one observation.

**Data Types: **`double`

| `table`

`Y`

— Class labels

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

This property is read-only.

Class labels corresponding to the observations in `X`

, returned as
a categorical array, cell array of character vectors, character array, logical vector,
or a numeric vector. Each row of `Y`

represents the classification of
the corresponding row of `X`

.

**Data Types: **`single`

| `double`

| `logical`

| `char`

| `string`

| `cell`

| `categorical`

## Object Functions

`compact` | Reduce size of classification ensemble model |

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

`crossval` | Cross-validate machine learning model |

`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 |

`resubEdge` | Resubstitution classification edge for classification ensemble model |

`resubLoss` | Resubstitution classification loss for classification ensemble model |

`resubMargin` | Resubstitution classification margins for classification ensemble model |

`resubPredict` | Classify observations in classification ensemble by resubstitution |

`resume` | Resume training of classification ensemble model |

`shapley` | Shapley values |

`testckfold` | Compare accuracies of two classification models by repeated cross-validation |

## Examples

### Train Boosted Classification Ensemble

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.

`Mdl.Trained`

is the property that stores a 100-by-1 cell vector of the trained classification trees (`CompactClassificationTree`

model objects) that compose the ensemble.

Plot a graph of the first trained classification tree.

view(Mdl.Trained{1},'Mode','graph')

By default, `fitcensemble`

grows shallow trees for boosted ensembles of trees.

Predict the label of the mean of `X`

.

predMeanX = predict(Mdl,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**

### R2023b: Model with discriminant analysis weak learners stores observations with missing predictor values

Starting in R2023b, training observations with missing predictor values are
included in the `X`

, `Y`

, and
`W`

data properties of classification ensemble models with
discriminant analysis weak learners. The `RowsUsed`

property
indicates the training observations stored in the model, rather than those used for
training. Observations with missing predictor values continue to be omitted from the
model training process.

In previous releases, the software omitted training observations that contained missing predictor values from the data properties of the model.

### 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`

)
and observation weights (`W`

) 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`

or `resubLoss`

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 and observation weights used for training in the
`Prior`

and `W`

properties, respectively. Starting
in R2022a, the software stores the user-specified cost matrix without modification, and stores normalized
prior probabilities and observation weights 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`

, `Prior`

, and `W`

properties:

The

`loss`

and`resubLoss`

functions use 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.The

`resubLoss`

and`resubEdge`

functions use the observation weights stored in the`W`

property.

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

`ClassificationTree`

| `fitcensemble`

| `CompactClassificationEnsemble`

| `view`

| `compareHoldout`

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