ClassificationPartitionedNeuralNetwork

Cross-validated classification neural network

Since R2026a

Description

ClassificationPartitionedNeuralNetwork is a set of classification neural network models trained on cross-validated folds. You can estimate the quality of the classification by using one or more kfold functions: kfoldPredict, kfoldLoss, kfoldMargin, kfoldEdge, and kfoldfun.

Each kfold function uses models trained on training-fold (in-fold) observations to predict the response for validation-fold (out-of-fold) observations. For example, when you use kfoldPredict with a k-fold cross-validated model, the software estimates a response for every observation using the model trained without that observation. For more information, see Partitioned Models.

Creation

You can create a ClassificationPartitionedNeuralNetwork object in two ways:

• Create a cross-validated model from a full classification model object by using the crossval object function.

• Create a cross-validated model by using the fitcnet function and specifying one of the name-value arguments CrossVal, KFold, Holdout, Leaveout, or CVPartition.

Properties

Cross-Validation Properties

CrossValidatedModel — Cross-validated model name

Read-only: 'NeuralNetwork'

This property is read-only.

Cross-validated model name, returned as 'NeuralNetwork'.

Data Types: char

KFold — Number of folds in model

Read-only: positive integer

This property is read-only.

Number of folds in the cross-validated model, returned as a positive integer.

Data Types: double

ModelParameters — Cross-validation parameter values

Read-only: EnsembleParams object

This property is read-only.

Cross-validation parameter values, returned as an EnsembleParams object. The parameter values correspond to the values of the name-value arguments used to cross-validate the neural network model. ModelParameters does not contain estimated parameters.

You can access the properties of ModelParameters using dot notation.

Partition — Partition used in cross-validation

Read-only: cvpartition object

This property is read-only.

Partition used in the cross-validation, returned as a cvpartition object.

Trained — Compact models trained on cross-validation folds

Read-only: cell array of CompactClassificationNeuralNetwork model objects

This property is read-only.

Compact models trained on the cross-validation folds, returned as a cell array of CompactClassificationNeuralNetwork model objects. Trained has k cells, where k is the number of folds.

Data Types: cell

Other Classification Properties

CategoricalPredictors — Categorical predictor indices

Read-only: vector of positive integers | []

This property is read-only.

Categorical predictor indices, returned 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: double

ClassNames — Unique class labels

Read-only: categorical array | character array | logical vector | numeric vector | cell array of character vectors

This property is read-only.

Unique class labels used in training, returned as a categorical or character array, logical or numeric vector, or cell array of character vectors. ClassNames has the same data type as the class labels Y. (The software treats string arrays as cell arrays of character vectors.) ClassNames also determines the class order.

Data Types: categorical | char | logical | single | double | cell

Cost — Misclassification costs

square numeric matrix

Misclassification costs, specified as a square numeric matrix. Cost has K rows and columns, where K is the number of classes.

Cost(i,j) is the cost of classifying a point into class j if its true class is i. The order of the rows and columns of Cost corresponds to the order of the classes in ClassNames.

You can change the cost matrix using dot notation. For example, for a cross-validated model CVMdl and a cost matrix costMatrix, you can specify:

CVMdl.Cost = costMatrix;

Data Types: double

NumObservations — Number of observations

Read-only: positive integer scalar

This property is read-only.

Number of observations in the training data stored in X and Y, returned as a positive integer scalar.

Data Types: double

PredictorNames — Predictor variable names

Read-only: cell array of character vectors

This property is read-only.

Predictor variable names, returned as a cell array of character vectors. The order of the elements in PredictorNames corresponds to the order in which the predictor names appear in the training data.

Data Types: cell

Prior — Prior probabilities for each class

Read-only: numeric vector

This property is read-only.

Prior probabilities for each class, returned as a numeric vector. The order of the elements of Prior corresponds to the order of the classes in ClassNames.

Data Types: double

ResponseName — Response variable name

Read-only: character vector

This property is read-only.

Response variable name, returned as a character vector.

Data Types: char

ScoreTransform — Score transformation function

character vector | string scalar | function handle

Score transformation function, specified as a character vector, string scalar, or function handle. ScoreTransform represents a built-in transformation function or a function handle for transforming predicted classification scores.

To change the score transformation function to function, for example, use dot notation.

• For a built-in function, enter a character vector or string scalar.

  Mdl.ScoreTransform = "function";

  This table lists the values for the available built-in functions.

  Value	Description
  "doublelogit"	1/(1 + e–2x)
  "invlogit"	log(x / (1 – x))
  "ismax"	Sets the score for the class with the largest score to 1, and sets the scores for all other classes to 0
  "logit"	1/(1 + e–x)
  "none" or "identity"	x (no transformation)
  "sign"	–1 for x < 0 0 for x = 0 1 for x > 0
  "symmetric"	2x – 1
  "symmetricismax"	Sets the score for the class with the largest score to 1, and sets the scores for all other classes to –1
  "symmetriclogit"	2/(1 + e–x) – 1

• For a MATLAB^® function or a function that you define, enter its function handle.

  Mdl.ScoreTransform = @function;

  function must accept a matrix (the original scores) and return a matrix of the same size (the transformed scores).

Data Types: char | string | function_handle

W — Observation weights

Read-only: numeric vector

This property is read-only.

Observation weights, returned as an n-by-1 numeric vector, where n is the number of observations (NumObservations).

Data Types: double

X — Unstandardized predictors

Read-only: numeric matrix | table

This property is read-only.

Unstandardized predictors used to cross-validate the model, returned as a numeric matrix or a table. X retains its original orientation, with observations in rows or columns depending on the value of the ObservationsIn name-value argument in the call to fitcnet.

Data Types: single | double | table

Y — Class labels

Read-only: numeric vector | logical vector | character array | cell array of character vectors | categorical array

This property is read-only.

Class labels used to cross-validate the model, returned as a numeric vector, logical vector, character array, cell array of character vectors, or categorical array. Each row of Y represents the classification of the corresponding observation in X.

Data Types: single | double | logical | char | cell | categorical

Object Functions

gather	Gather properties of Statistics and Machine Learning Toolbox object from GPU
kfoldEdge	Classification edge for cross-validated classification model
kfoldLoss	Classification loss for cross-validated classification model
kfoldMargin	Classification margins for cross-validated classification model
kfoldPredict	Classify observations in cross-validated classification model
kfoldfun	Cross-validate function for classification

Examples

Cross-Validate Neural Network Classifier

Cross-validate a neural network classifier using the default 10-fold cross-validation.

Load the ionosphere data set, which includes radar signal data. X contains the predictor data, and Y is the response variable, whose values represent either good ("g") or bad ("b") radar signals.

load ionosphere

Train a neural network classifier using the fitcnet function. Specify 35 outputs for the first fully connected layer and 20 outputs for the second fully connected layer.

Mdl = fitcnet(X,Y,LayerSizes=[35 20])

Mdl = 
  ClassificationNeuralNetwork
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
          NumObservations: 351
               LayerSizes: [35 20]
              Activations: 'relu'
    OutputLayerActivation: 'softmax'
                   Solver: 'LBFGS'
          ConvergenceInfo: [1×1 struct]
          TrainingHistory: [72×7 table]


  Properties, Methods

Mdl is a trained ClassificationNeuralNetwork model object.

Cross-validate the classifier using 10-fold cross-validation. During cross-validation, the software completes these steps:

1. Randomly partition the data into 10 sets (folds) of equal size.

2. For each set, reserve the set as validation data, and train a neural network classifier on the other nine sets.

3. Store the 10 trained models in a 10-by-1 cell vector in the Trained property of the cross-validated model object.

CVMdl = crossval(Mdl)

CVMdl = 
  ClassificationPartitionedNeuralNetwork
    CrossValidatedModel: 'NeuralNetwork'
         PredictorNames: {'x1'  'x2'  'x3'  'x4'  'x5'  'x6'  'x7'  'x8'  'x9'  'x10'  'x11'  'x12'  'x13'  'x14'  'x15'  'x16'  'x17'  'x18'  'x19'  'x20'  'x21'  'x22'  'x23'  'x24'  'x25'  'x26'  'x27'  'x28'  'x29'  'x30'  'x31'  'x32'  'x33'  'x34'}
           ResponseName: 'Y'
        NumObservations: 351
                  KFold: 10
              Partition: [1×1 cvpartition]
             ClassNames: {'b'  'g'}
         ScoreTransform: 'none'


  Properties, Methods

CVMdl is a ClassificationPartitionedNeuralNetwork object.

Display the first model in CVMdl.Trained.

FirstModel = CVMdl.Trained{1}

FirstModel = 
  CompactClassificationNeuralNetwork
               LayerSizes: [35 20]
              Activations: 'relu'
    OutputLayerActivation: 'softmax'


  Properties, Methods

FirstModel is the first of the 10 trained models contained in CVMdl. The trained models are all CompactClassificationNeuralNetwork objects.

After creating a cross-validated neural network model object, you can estimate the generalized classification error by passing the model to kfoldLoss.

Algorithms

Partitioned Models

You can create partitioned models by using k-fold cross-validation, holdout validation, leave-one-out cross-validation, or resubstitution.

• k-fold cross-validation — The software divides the observations into KFold disjoint folds, each of which has approximately the same number of observations. The software trains KFold models (Trained), and each model is trained on KFold – 1 of the folds. When you use kfoldPredict, each model predicts the response values for the remaining fold.

• Holdout validation — The software partitions the observations into a training set and a validation set. The software trains one model (Trained) using the training set. When you use kfoldPredict, the model predicts the response values for the validation set.

• Leave-one-out cross-validation — The software creates NumObservations folds, where each observation is a fold. The software trains NumObservations models (Trained), and each model is trained on NumObservations – 1 of the folds. When you use kfoldPredict, each model predicts the response for the remaining fold (observation).

• Resubstitution — The software does not partition the data. The software trains one model (Trained) on the entire data set. When you use kfoldPredict, the model predicts the response values for all observations.

Extended Capabilities

GPU Arrays

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

The object functions of a ClassificationPartitionedNeuralNetwork model fully support GPU arrays.

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

Version History

Introduced in R2026a