# fitrnet

Train neural network regression model

## Syntax

``Mdl = fitrnet(Tbl,ResponseVarName)``
``Mdl = fitrnet(Tbl,formula)``
``Mdl = fitrnet(Tbl,Y)``
``Mdl = fitrnet(X,Y)``
``Mdl = fitrnet(___,Name,Value)``

## Description

Use `fitrnet` to train a feedforward, fully connected neural network for regression. The first fully connected layer of the neural network has a connection from the network input (predictor data), and each subsequent layer has a connection from the previous layer. Each fully connected layer multiplies the input by a weight matrix and then adds a bias vector. An activation function follows each fully connected layer, excluding the last. The final fully connected layer produces the network's output, namely predicted response values. For more information, see Neural Network Structure.

example

````Mdl = fitrnet(Tbl,ResponseVarName)` returns a neural network regression model `Mdl` trained using the predictors in the table `Tbl` and the response values in the `ResponseVarName` table variable.```
````Mdl = fitrnet(Tbl,formula)` returns a neural network regression model trained using the sample data in the table `Tbl`. The input argument `formula` is an explanatory model of the response and a subset of the predictor variables in `Tbl` used to fit `Mdl`.```
````Mdl = fitrnet(Tbl,Y)` returns a neural network regression model using the predictor variables in the table `Tbl` and the response values in vector `Y`.```

example

````Mdl = fitrnet(X,Y)` returns a neural network regression model trained using the predictors in the matrix `X` and the response values in vector `Y`.```

example

````Mdl = fitrnet(___,Name,Value)` specifies options using one or more name-value arguments in addition to any of the input argument combinations in previous syntaxes. For example, you can adjust the number of outputs and the activation functions for the fully connected layers by specifying the `LayerSizes` and `Activations` name-value arguments.```

## Examples

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Train a neural network regression model, and assess the performance of the model on a test set.

Load the `carbig` data set, which contains measurements of cars made in the 1970s and early 1980s. Create a table containing the predictor variables `Acceleration`, `Displacement`, and so on, as well as the response variable `MPG`.

```load carbig cars = table(Acceleration,Displacement,Horsepower, ... Model_Year,Origin,Weight,MPG);```

Partition the data into training and test sets. Use approximately 80% of the observations to train a neural network model, and 20% of the observations to test the performance of the trained model on new data. Use `cvpartition` to partition the data.

```rng("default") % For reproducibility of the data partition c = cvpartition(length(MPG),"Holdout",0.20); trainingIdx = training(c); % Training set indices carsTrain = cars(trainingIdx,:); testIdx = test(c); % Test set indices carsTest = cars(testIdx,:);```

Train a neural network regression model by passing the `carsTrain` training data to the `fitrnet` function. For better results, specify to standardize the predictor data.

`Mdl = fitrnet(carsTrain,"MPG","Standardize",true)`
```Mdl = RegressionNeuralNetwork PredictorNames: {'Acceleration' 'Displacement' 'Horsepower' 'Model_Year' 'Origin' 'Weight'} ResponseName: 'MPG' CategoricalPredictors: 5 ResponseTransform: 'none' NumObservations: 314 LayerSizes: 10 Activations: 'relu' OutputLayerActivation: 'linear' Solver: 'LBFGS' ConvergenceInfo: [1×1 struct] TrainingHistory: [1000×7 table] Properties, Methods ```

`Mdl` is a trained `RegressionNeuralNetwork` model. You can use dot notation to access the properties of `Mdl`. For example, you can specify `Mdl.TrainingHistory` to get more information about the training history of the neural network model.

Evaluate the performance of the regression model on the test set by computing the test mean squared error (MSE). Smaller MSE values indicate better performance.

`testMSE = loss(Mdl,carsTest,"MPG")`
```testMSE = 16.6154 ```

Specify the structure of the neural network regression model, including the size of the fully connected layers.

Load the `carbig` data set, which contains measurements of cars made in the 1970s and early 1980s. Create a matrix `X` containing the predictor variables `Acceleration`, `Cylinders`, and so on. Store the response variable `MPG` in the variable `Y`.

```load carbig X = [Acceleration Cylinders Displacement Weight]; Y = MPG;```

Partition the data into training data (`XTrain` and `YTrain`) and test data (`XTest` and `YTest`). Reserve approximately 20% of the observations for testing, and use the rest of the observations for training.

```rng("default") % For reproducibility of the partition c = cvpartition(length(Y),"Holdout",0.20); trainingIdx = training(c); % Indices for the training set XTrain = X(trainingIdx,:); YTrain = Y(trainingIdx); testIdx = test(c); % Indices for the test set XTest = X(testIdx,:); YTest = Y(testIdx);```

Train a neural network regression model. Specify to standardize the predictor data, and to have 30 outputs in the first fully connected layer and 10 outputs in the second fully connected layer. By default, both layers use a rectified linear unit (ReLU) activation function. You can change the activation functions for the fully connected layers by using the `Activations` name-value argument.

```Mdl = fitrnet(XTrain,YTrain,"Standardize",true, ... "LayerSizes",[30 10])```
```Mdl = RegressionNeuralNetwork ResponseName: 'Y' CategoricalPredictors: [] ResponseTransform: 'none' NumObservations: 318 LayerSizes: [30 10] Activations: 'relu' OutputLayerActivation: 'linear' Solver: 'LBFGS' ConvergenceInfo: [1×1 struct] TrainingHistory: [1000×7 table] Properties, Methods ```

Access the weights and biases for the fully connected layers of the trained model by using the `LayerWeights` and `LayerBiases` properties of `Mdl`. The first two elements of each property correspond to the values for the first two fully connected layers, and the third element corresponds to the values for the final fully connected layer for regression. For example, display the weights and biases for the first fully connected layer.

`Mdl.LayerWeights{1}`
```ans = 30×4 -1.0617 0.1287 0.0797 0.4648 -0.6497 -1.4565 -2.6026 2.6962 -0.6420 0.2744 -0.0234 -0.0252 -1.9727 -0.4665 -0.5833 0.9371 -0.4373 0.1607 0.3930 0.7859 0.5091 -0.0032 -0.6503 -1.6694 0.0123 -0.2624 -2.2928 -1.0965 -0.1386 1.2747 0.4085 0.5395 -0.1755 1.5641 -3.1896 -1.1336 0.4401 0.4942 1.8957 -1.1617 ⋮ ```
`Mdl.LayerBiases{1}`
```ans = 30×1 -1.3086 -1.6205 -0.7815 1.5382 -0.5256 1.2394 -2.3078 -1.0709 -1.8898 1.9443 ⋮ ```

The final fully connected layer has one output. The number of layer outputs corresponds to the first dimension of the layer weights and layer biases.

`size(Mdl.LayerWeights{end})`
```ans = 1×2 1 10 ```
`size(Mdl.LayerBiases{end})`
```ans = 1×2 1 1 ```

To estimate the performance of the trained model, compute the test set mean squared error (MSE) for `Mdl`. Smaller MSE values indicate better performance.

`testMSE = loss(Mdl,XTest,YTest)`
```testMSE = 17.2022 ```

Compare the predicted test set response values to the true response values. Plot the predicted miles per gallon (MPG) along the vertical axis and the true MPG along the horizontal axis. Points on the reference line indicate correct predictions. A good model produces predictions that are scattered near the line.

```testPredictions = predict(Mdl,XTest); plot(YTest,testPredictions,".") hold on plot(YTest,YTest) hold off xlabel("True MPG") ylabel("Predicted MPG")```

At each iteration of the training process, compute the validation loss of the neural network. Stop the training process early if the validation loss reaches a reasonable minimum.

Load the `patients` data set. Create a table from the data set. Each row corresponds to one patient, and each column corresponds to a diagnostic variable. Use the `Systolic` variable as the response variable, and the rest of the variables as predictors.

```load patients tbl = table(Age,Diastolic,Gender,Height,Smoker,Weight,Systolic);```

Separate the data into a training set `tblTrain` and a validation set `tblValidation`. The software reserves approximately 30% of the observations for the validation data set and uses the rest of the observations for the training data set.

```rng("default") % For reproducibility of the partition c = cvpartition(size(tbl,1),"Holdout",0.30); trainingIndices = training(c); validationIndices = test(c); tblTrain = tbl(trainingIndices,:); tblValidation = tbl(validationIndices,:);```

Train a neural network regression model by using the training set. Specify the `Systolic` column of `tblTrain` as the response variable. Evaluate the model at each iteration by using the validation set. Specify to display the training information at each iteration by using the `Verbose` name-value argument. By default, the training process ends early if the validation loss is greater than or equal to the minimum validation loss computed so far, six times in a row. To change the number of times the validation loss is allowed to be greater than or equal to the minimum, specify the `ValidationPatience` name-value argument.

```Mdl = fitrnet(tblTrain,"Systolic", ... "ValidationData",tblValidation, ... "Verbose",1);```
```|==========================================================================================| | Iteration | Train Loss | Gradient | Step | Iteration | Validation | Validation | | | | | | Time (sec) | Loss | Checks | |==========================================================================================| | 1| 516.021993| 3220.880047| 0.644473| 0.005193| 568.289202| 0| | 2| 313.056754| 229.931405| 0.067026| 0.002658| 304.023695| 0| | 3| 308.461807| 277.166516| 0.011122| 0.001363| 296.935608| 0| | 4| 262.492770| 844.627934| 0.143022| 0.000531| 240.559640| 0| | 5| 169.558740| 1131.714363| 0.336463| 0.000652| 152.531663| 0| | 6| 89.134368| 362.084104| 0.382677| 0.001059| 83.147478| 0| | 7| 83.309729| 994.830303| 0.199923| 0.000515| 76.634122| 0| | 8| 70.731524| 327.637362| 0.041366| 0.000361| 66.421750| 0| | 9| 66.650091| 124.369963| 0.125232| 0.000380| 65.914063| 0| | 10| 66.404753| 36.699328| 0.016768| 0.000363| 65.357335| 0| |==========================================================================================| | Iteration | Train Loss | Gradient | Step | Iteration | Validation | Validation | | | | | | Time (sec) | Loss | Checks | |==========================================================================================| | 11| 66.357143| 46.712988| 0.009405| 0.001130| 65.306106| 0| | 12| 66.268225| 54.079264| 0.007953| 0.001023| 65.234391| 0| | 13| 65.788550| 99.453225| 0.030942| 0.000436| 64.869708| 0| | 14| 64.821095| 186.344649| 0.048078| 0.000295| 64.191533| 0| | 15| 62.353896| 319.273873| 0.107160| 0.000290| 62.618374| 0| | 16| 57.836593| 447.826470| 0.184985| 0.000287| 60.087065| 0| | 17| 51.188884| 524.631067| 0.253062| 0.000287| 56.646294| 0| | 18| 41.755601| 189.072516| 0.318515| 0.000286| 49.046823| 0| | 19| 37.539854| 78.602559| 0.382284| 0.000290| 44.633562| 0| | 20| 36.845322| 151.837884| 0.211286| 0.000286| 47.291367| 1| |==========================================================================================| | Iteration | Train Loss | Gradient | Step | Iteration | Validation | Validation | | | | | | Time (sec) | Loss | Checks | |==========================================================================================| | 21| 36.218289| 62.826818| 0.142748| 0.000362| 46.139104| 2| | 22| 35.776921| 53.606315| 0.215188| 0.000321| 46.170460| 3| | 23| 35.729085| 24.400342| 0.060096| 0.001023| 45.318023| 4| | 24| 35.622031| 9.602277| 0.121153| 0.000289| 45.791861| 5| | 25| 35.573317| 10.735070| 0.126854| 0.000291| 46.062826| 6| |==========================================================================================| ```

Create a plot that compares the training mean squared error (MSE) and the validation MSE at each iteration. By default, `fitrnet` stores the loss information inside the `TrainingHistory` property of the object `Mdl`. You can access this information by using dot notation.

```iteration = Mdl.TrainingHistory.Iteration; trainLosses = Mdl.TrainingHistory.TrainingLoss; valLosses = Mdl.TrainingHistory.ValidationLoss; plot(iteration,trainLosses,iteration,valLosses) legend(["Training","Validation"]) xlabel("Iteration") ylabel("Mean Squared Error")```

Check the iteration that corresponds to the minimum validation MSE. The final returned model `Mdl` is the model trained at this iteration.

```[~,minIdx] = min(valLosses); iteration(minIdx)```
```ans = 19 ```

Assess the cross-validation loss of neural network models with different regularization strengths, and choose the regularization strength corresponding to the best performing model.

Load the `carbig` data set, which contains measurements of cars made in the 1970s and early 1980s. Create a table containing the predictor variables `Acceleration`, `Displacement`, and so on, as well as the response variable `MPG`. Remove observations from the table with missing values.

```load carbig tbl = table(Acceleration,Displacement,Horsepower, ... Model_Year,Origin,Weight,MPG); cars = rmmissing(tbl);```

Create a `cvpartition` object for 5-fold cross-validation. `cvp` partitions the data into five folds, where each fold has roughly the same number of observations. Set the random seed to the default value for reproducibility of the partition.

```rng("default") n = size(cars,1); cvp = cvpartition(n,"KFold",5);```

Compute the cross-validation mean squared error (MSE) for neural network regression models with different regularization strengths. Try regularization strengths on the order of 1/n, where n is the number of observations. Specify to standardize the data before training the neural network models.

`1/n`
```ans = 0.0026 ```
```lambda = (0:0.5:5)*1e-3; cvloss = zeros(length(lambda),1); for i = 1:length(lambda) cvMdl = fitrnet(cars,"MPG","Lambda",lambda(i), ... "CVPartition",cvp,"Standardize",true); cvloss(i) = kfoldLoss(cvMdl); end```

Plot the results. Find the regularization strength corresponding to the lowest cross-validation MSE.

```plot(lambda,cvloss) xlabel("Regularization Strength") ylabel("Cross-Validation Loss")```

```[~,idx] = min(cvloss); bestLambda = lambda(idx)```
```bestLambda = 0 ```

Train a neural network regression model using the `bestLambda` regularization strength.

```Mdl = fitrnet(cars,"MPG","Lambda",bestLambda, ... "Standardize",true)```
```Mdl = RegressionNeuralNetwork PredictorNames: {'Acceleration' 'Displacement' 'Horsepower' 'Model_Year' 'Origin' 'Weight'} ResponseName: 'MPG' CategoricalPredictors: 5 ResponseTransform: 'none' NumObservations: 392 LayerSizes: 10 Activations: 'relu' OutputLayerActivation: 'linear' Solver: 'LBFGS' ConvergenceInfo: [1×1 struct] TrainingHistory: [1000×7 table] Properties, Methods ```

## Input Arguments

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Sample data used to train the model, specified as a table. Each row of `Tbl` corresponds to one observation, and each column corresponds to one predictor variable. Optionally, `Tbl` can contain one additional column for the response variable. Multicolumn variables and cell arrays other than cell arrays of character vectors are not allowed.

• If `Tbl` contains the response variable, and you want to use all remaining variables in `Tbl` as predictors, then specify the response variable by using `ResponseVarName`.

• If `Tbl` contains the response variable, and you want to use only a subset of the remaining variables in `Tbl` as predictors, then specify a formula by using `formula`.

• If `Tbl` does not contain the response variable, then specify a response variable by using `Y`. The length of the response variable and the number of rows in `Tbl` must be equal.

Data Types: `table`

Response variable name, specified as the name of a variable in `Tbl`. The response variable must be a numeric vector.

You must specify `ResponseVarName` as a character vector or string scalar. For example, if `Tbl` stores the response variable `Y` as `Tbl.Y`, then specify it as `'Y'`. Otherwise, the software treats all columns of `Tbl`, including `Y`, as predictors when training the model.

Data Types: `char` | `string`

Explanatory model of the response variable and a subset of the predictor variables, specified as a character vector or string scalar in the form `'Y~x1+x2+x3'`. In this form, `Y` represents the response variable, and `x1`, `x2`, and `x3` represent the predictor variables.

To specify a subset of variables in `Tbl` as predictors for training the model, use a formula. If you specify a formula, then the software does not use any variables in `Tbl` that do not appear in `formula`.

The variable names in the formula must be both variable names in `Tbl` (`Tbl.Properties.VariableNames`) and valid MATLAB® identifiers. You can verify the variable names in `Tbl` by using the `isvarname` function. If the variable names are not valid, then you can convert them by using the `matlab.lang.makeValidName` function.

Data Types: `char` | `string`

Response data, specified as a numeric vector. The length of `Y` must be equal to the number of observations in `X` or `Tbl`.

Data Types: `single` | `double`

Predictor data used to train the model, specified as a numeric matrix.

By default, the software treats each row of `X` as one observation, and each column as one predictor.

The length of `Y` and the number of observations in `X` must be equal.

To specify the names of the predictors in the order of their appearance in `X`, use the `PredictorNames` name-value argument.

Note

If you orient your predictor matrix so that observations correspond to columns and specify `'ObservationsIn','columns'`, then you might experience a significant reduction in computation time.

Data Types: `single` | `double`

Note

The software treats `NaN`, empty character vector (`''`), empty string (`""`), `<missing>`, and `<undefined>` elements as missing values, and removes observations with any of these characteristics:

• Missing value in the response (for example, `Y` or `ValidationData``{2}`)

• At least one missing value in a predictor observation (for example, row in `X` or `ValidationData{1}`)

• `NaN` value or `0` weight (for example, value in `Weights` or `ValidationData{3}`)

### Name-Value Pair Arguments

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`.

Example: ```fitrnet(X,Y,'LayerSizes',[10 10],'Activations',["relu","tanh"])``` specifies to create a neural network with two fully connected layers, each with 10 outputs. The first layer uses a rectified linear unit (ReLU) activation function, and the second uses a hyperbolic tangent activation function.
Neural Network Options

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Sizes of the fully connected layers in the neural network model, specified as a positive integer vector. The ith element of `LayerSizes` is the number of outputs in the ith fully connected layer of the neural network model.

`LayerSizes` does not include the size of the final fully connected layer. For more information, see Neural Network Structure.

Example: `'LayerSizes',[100 25 10]`

Activation functions for the fully connected layers of the neural network model, specified as a character vector, string scalar, string array, or cell array of character vectors with values from this table.

ValueDescription
`'relu'`

Rectified linear unit (ReLU) function — Performs a threshold operation on each element of the input, where any value less than zero is set to zero, that is,

`$f\left(x\right)=\left\{\begin{array}{cc}x,& x\ge 0\\ 0,& x<0\end{array}$`

`'tanh'`

Hyperbolic tangent (tanh) function — Applies the `tanh` function to each input element

`'sigmoid'`

Sigmoid function — Performs the following operation on each input element:

`$f\left(x\right)=\frac{1}{1+{e}^{-x}}$`

`'none'`

Identity function — Returns each input element without performing any transformation, that is, f(x) = x

• If you specify one activation function only, then `Activations` is the activation function for every fully connected layer of the neural network model, excluding the final fully connected layer (see Neural Network Structure).

• If you specify an array of activation functions, then the ith element of `Activations` is the activation function for the ith layer of the neural network model.

Example: `'Activations','sigmoid'`

Function to initialize the fully connected layer weights, specified as `'glorot'` or `'he'`.

ValueDescription
`'glorot'`Initialize the weights with the Glorot initializer [1] (also known as the Xavier initializer). For each layer, the Glorot initalizer independently samples from a uniform distribution with zero mean and variable `2/(I+O)`, where `I` is the input size and `O` is the output size for the layer.
`'he'`Initialize the weights with the He initializer [2]. For each layer, the He initializer samples from a normal distribution with zero mean and variance `2/I`, where `I` is the input size for the layer.

Example: `'LayerWeightsFunction','he'`

Type of initial fully connected layer biases, specified as `'zeros'` or `'ones'`.

• If you specify the value `'zeros'`, then each fully connected layer has an initial bias of 0.

• If you specify the value `'ones'`, then each fully connected layer has an initial bias of 1.

Example: `'LayerBiasesInitializer','ones'`

Data Types: `char` | `string`

Predictor data observation dimension, specified as `'rows'` or `'columns'`.

Note

If you orient your predictor matrix so that observations correspond to columns and specify `'ObservationsIn','columns'`, then you might experience a significant reduction in computation time. You cannot specify `'ObservationsIn','columns'` for predictor data in a table.

Example: `'ObservationsIn','columns'`

Data Types: `char` | `string`

Regularization term strength, specified as a nonnegative scalar. The software composes the objective function for minimization from the mean squared error (MSE) loss function and the ridge (L2) penalty term.

Example: `'Lambda',1e-4`

Data Types: `single` | `double`

Flag to standardize the predictor data, specified as a numeric or logical `0` (`false`) or `1` (`true`). If you set `Standardize` to `true`, then the software centers and scales each numeric predictor variable by the corresponding column mean and standard deviation. The software does not standardize the categorical predictors.

Example: `'Standardize',true`

Data Types: `single` | `double` | `logical`

Convergence Control Options

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Verbosity level, specified as `0` or `1`. The `'Verbose'` name-value argument controls the amount of diagnostic information that `fitrnet` displays at the command line.

ValueDescription
`0``fitrnet` does not display diagnostic information.
`1``fitrnet` periodically displays diagnostic information.

By default, `StoreHistory` is set to `true` and `fitrnet` stores the diagnostic information inside of `Mdl`. Use `Mdl.TrainingHistory` to access the diagnostic information.

Example: `'Verbose',1`

Data Types: `single` | `double`

Frequency of verbose printing, which is the number of iterations between printing to the command window, specified as a positive integer scalar. A value of 1 indicates to print diagnostic information at every iteration.

Note

To use this name-value argument, set `Verbose` to `1`.

Example: `'VerboseFrequency',5`

Data Types: `single` | `double`

Flag to store the training history, specified as a numeric or logical `0` (`false`) or `1` (`true`). If `StoreHistory` is set to `true`, then the software stores diagnostic information inside of `Mdl`, which you can access by using `Mdl.TrainingHistory`.

Example: `'StoreHistory',false`

Data Types: `single` | `double` | `logical`

Maximum number of training iterations, specified as a positive integer scalar.

The software returns a trained model regardless of whether the training routine successfully converges. `Mdl.ConvergenceInfo` contains convergence information.

Example: `'IterationLimit',1e8`

Data Types: `single` | `double`

Relative gradient tolerance, specified as a nonnegative scalar.

Let ${ℒ}_{t}$ be the loss function at training iteration t, $\nabla {ℒ}_{t}$ be the gradient of the loss function with respect to the weights and biases at iteration t, and $\nabla {ℒ}_{0}$ be the gradient of the loss function at an initial point. If $\mathrm{max}|\nabla {ℒ}_{t}|\le a\cdot \text{GradientTolerance}$, where $a=\mathrm{max}\left(1,\mathrm{min}|{ℒ}_{t}|,\mathrm{max}|\nabla {ℒ}_{0}|\right)$, then the training process terminates.

Example: `'GradientTolerance',1e-5`

Data Types: `single` | `double`

Loss tolerance, specified as a nonnegative scalar.

If the function loss at some iteration is smaller than `LossTolerance`, then the training process terminates.

Example: `'LossTolerance',1e-8`

Data Types: `single` | `double`

Step size tolerance, specified as a nonnegative scalar.

If the step size at some iteration is smaller than `StepTolerance`, then the training process terminates.

Example: `'StepTolerance',1e-4`

Data Types: `single` | `double`

Validation data for training convergence detection, specified as a cell array or table.

During the training process, the software periodically estimates the validation loss by using `ValidationData`. If the validation loss increases more than `ValidationPatience` times in a row, then the software terminates the training.

You can specify `ValidationData` as a table if you use a table `Tbl` of predictor data that contains the response variable. In this case, `ValidationData` must contain the same predictors and response contained in `Tbl`. The software does not apply weights to observations, even if `Tbl` contains a vector of weights. To specify weights, you must specify `ValidationData` as a cell array.

If you specify `ValidationData` as a cell array, then it must have the following format:

• `ValidationData{1}` must have the same data type and orientation as the predictor data. That is, if you use a predictor matrix `X`, then `ValidationData{1}` must be an m-by-p or p-by-m matrix of predictor data that has the same orientation as `X`. The predictor variables in the training data `X` and `ValidationData{1}` must correspond. Similarly, if you use a predictor table `Tbl` of predictor data, then `ValidationData{1}` must be a table containing the same predictor variables contained in `Tbl`. The number of observations in `ValidationData{1}` and the predictor data can vary.

• `ValidationData{2}` must match the data type and format of the response variable, either `Y` or `ResponseVarName`. If `ValidationData{2}` is an array of responses, then it must have the same number of elements as the number of observations in `ValidationData{1}`. If `ValidationData{1}` is a table, then `ValidationData{2}` can be the name of the response variable in the table. If you want to use the same `ResponseVarName` or `formula`, you can specify `ValidationData{2}` as `[]`.

• Optionally, you can specify `ValidationData{3}` as an m-dimensional numeric vector of observation weights or the name of a variable in the table `ValidationData{1}` that contains observation weights. The software normalizes the weights with the validation data so that they sum to 1.

If you specify `ValidationData` and want to display the validation loss at the command line, set `Verbose` to `1`.

Number of iterations between validation evaluations, specified as a positive integer scalar. A value of 1 indicates to evaluate validation metrics at every iteration.

Note

To use this name-value argument, you must specify `ValidationData`.

Example: `'ValidationFrequency',5`

Data Types: `single` | `double`

Stopping condition for validation evaluations, specified as a nonnegative integer scalar. Training stops if the validation loss is greater than or equal to the minimum validation loss computed so far, `ValidationPatience` times in a row. You can check the `Mdl.TrainingHistory` table to see the running total of times that the validation loss is greater than or equal to the minimum (`Validation Checks`).

Example: `'ValidationPatience',10`

Data Types: `single` | `double`

Other Regression Options

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Categorical predictors list, specified as one of the values in this table. The descriptions assume that the predictor data has observations in rows and predictors in columns.

ValueDescription
Vector of positive integers

Each entry in the vector is an index value corresponding to the column of the predictor data that contains a categorical variable. The index values are between 1 and `p`, where `p` is the number of predictors used to train the model.

If `fitrnet` uses a subset of input variables as predictors, then the function indexes the predictors using only the subset. The `'CategoricalPredictors'` values do not count the response variable, the observation weight variable, and any other variables that the function does not use.

Logical vector

A `true` entry means that the corresponding column of predictor data is a categorical variable. The length of the vector is `p`.

Character matrixEach row of the matrix is the name of a predictor variable. The names must match the entries in `PredictorNames`. Pad the names with extra blanks so each row of the character matrix has the same length.
String array or cell array of character vectorsEach element in the array is the name of a predictor variable. The names must match the entries in `PredictorNames`.
`'all'`All predictors are categorical.

By default, if the predictor data is in a table (`Tbl`), `fitrnet` assumes that a variable is categorical if it is a logical vector, categorical vector, character array, string array, or cell array of character vectors. If the predictor data is a matrix (`X`), `fitrnet` assumes that all predictors are continuous. To identify any other predictors as categorical predictors, specify them by using the `'CategoricalPredictors'` name-value argument.

For the identified categorical predictors, `fitrnet` creates dummy variables using two different schemes, depending on whether a categorical variable is unordered or ordered. For an unordered categorical variable, `fitrnet` creates one dummy variable for each level of the categorical variable. For an ordered categorical variable, `fitrnet` creates one less dummy variable than the number of categories. For details, see Automatic Creation of Dummy Variables.

Example: `'CategoricalPredictors','all'`

Data Types: `single` | `double` | `logical` | `char` | `string` | `cell`

Predictor variable names, specified as a string array of unique names or cell array of unique character vectors. The functionality of `'PredictorNames'` depends on the way you supply the training data.

• If you supply `X` and `Y`, then you can use `'PredictorNames'` to assign names to the predictor variables in `X`.

• The order of the names in `PredictorNames` must correspond to the predictor order in `X`. Assuming that `X` has the default orientation, with observations in rows and predictors in columns, `PredictorNames{1}` is the name of `X(:,1)`, `PredictorNames{2}` is the name of `X(:,2)`, and so on. Also, `size(X,2)` and `numel(PredictorNames)` must be equal.

• By default, `PredictorNames` is `{'x1','x2',...}`.

• If you supply `Tbl`, then you can use `'PredictorNames'` to choose which predictor variables to use in training. That is, `fitrnet` uses only the predictor variables in `PredictorNames` and the response variable during training.

• `PredictorNames` must be a subset of `Tbl.Properties.VariableNames` and cannot include the name of the response variable.

• By default, `PredictorNames` contains the names of all predictor variables.

• A good practice is to specify the predictors for training using either `'PredictorNames'` or `formula`, but not both.

Example: `'PredictorNames',{'SepalLength','SepalWidth','PetalLength','PetalWidth'}`

Data Types: `string` | `cell`

Response variable name, specified as a character vector or string scalar.

Example: `'ResponseName','response'`

Data Types: `char` | `string`

Observation weights, specified as a nonnegative numeric vector or the name of a variable in `Tbl`. The software weights each observation in `X` or `Tbl` with the corresponding value in `Weights`. The length of `Weights` must equal the number of observations in `X` or `Tbl`.

If you specify the input data as a table `Tbl`, then `Weights` can be the name of a variable in `Tbl` that contains a numeric vector. In this case, you must specify `Weights` as a character vector or string scalar. For example, if weights vector `W` is stored as `Tbl.W`, then specify it as `'W'`. Otherwise, the software treats all columns of `Tbl`, including `W`, as predictors when training the model.

By default, `Weights` is `ones(n,1)`, where `n` is the number of observations in `X` or `Tbl`.

`fitrnet` normalizes the weights to sum to 1.

Data Types: `single` | `double` | `char` | `string`

Cross-Validation Options

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Flag to train a cross-validated model, specified as `'on'` or `'off'`.

If you specify `'on'`, then the software trains a cross-validated model with 10 folds.

You can override this cross-validation setting using the `CVPartition`, `Holdout`, `KFold`, or `Leaveout` name-value argument. You can use only one cross-validation name-value argument at a time to create a cross-validated model.

Alternatively, cross-validate later by passing `Mdl` to `crossval`.

Example: `'Crossval','on'`

Data Types: `char` | `string`

Cross-validation partition, specified as a `cvpartition` partition object created by `cvpartition`. The partition object specifies the type of cross-validation and the indexing for the training and validation sets.

To create a cross-validated model, you can specify only one of these four name-value arguments: `CVPartition`, `Holdout`, `KFold`, or `Leaveout`.

Example: Suppose you create a random partition for 5-fold cross-validation on 500 observations by using `cvp = cvpartition(500,'KFold',5)`. Then, you can specify the cross-validated model by using `'CVPartition',cvp`.

Fraction of the data used for holdout validation, specified as a scalar value in the range (0,1). If you specify `'Holdout',p`, then the software completes these steps:

1. Randomly select and reserve `p*100`% of the data as validation data, and train the model using the rest of the data.

2. Store the compact, trained model in the `Trained` property of the cross-validated model.

To create a cross-validated model, you can specify only one of these four name-value arguments: `CVPartition`, `Holdout`, `KFold`, or `Leaveout`.

Example: `'Holdout',0.1`

Data Types: `double` | `single`

Number of folds to use in a cross-validated model, specified as a positive integer value greater than 1. If you specify `'KFold',k`, then the software completes these steps:

1. Randomly partition the data into `k` sets.

2. For each set, reserve the set as validation data, and train the model using the other `k` – 1 sets.

3. Store the `k` compact, trained models in a `k`-by-1 cell vector in the `Trained` property of the cross-validated model.

To create a cross-validated model, you can specify only one of these four name-value arguments: `CVPartition`, `Holdout`, `KFold`, or `Leaveout`.

Example: `'KFold',5`

Data Types: `single` | `double`

Leave-one-out cross-validation flag, specified as `'on'` or `'off'`. If you specify `'Leaveout','on'`, then for each of the n observations (where n is the number of observations, excluding missing observations, specified in the `NumObservations` property of the model), the software completes these steps:

1. Reserve the one observation as validation data, and train the model using the other n – 1 observations.

2. Store the n compact, trained models in an n-by-1 cell vector in the `Trained` property of the cross-validated model.

To create a cross-validated model, you can specify only one of these four name-value arguments: `CVPartition`, `Holdout`, `KFold`, or `Leaveout`.

Example: `'Leaveout','on'`

## Output Arguments

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Trained neural network regression model, returned as a `RegressionNeuralNetwork` or `RegressionPartitionedModel`object.

If you set any of the name-value arguments `CrossVal`, `CVPartition`, `Holdout`, `KFold`, or `Leaveout`, then `Mdl` is a `RegressionPartitionedModel` object. Otherwise, `Mdl` is a `RegressionNeuralNetwork` model.

To reference properties of `Mdl`, use dot notation.

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### Neural Network Structure

The default neural network regression model has the following layer structure.

StructureDescription

Input — This layer corresponds to the predictor data in `Tbl` or `X`.

First fully connected layer — This layer has 10 outputs by default.

• You can widen the layer or add more fully connected layers to the network by specifying the `LayerSizes` name-value argument.

• You can find the weights and biases for this layer in the `Mdl.LayerWeights{1}` and `Mdl.LayerBiases{1}` properties of `Mdl`, respectively.

ReLU activation function — `fitrnet` applies this activation function to the first fully connected layer.

Final fully connected layer — This layer has one output.

• You can find the weights and biases for this layer in the `Mdl.LayerWeights{end}` and `Mdl.LayerBiases{end}` properties of `Mdl`, respectively.

Output — This layer corresponds to the predicted response values.

For an example that shows how a regression neural network model with this layer structure returns predictions, see Predict Using Layer Structure of Regression Neural Network Model.

## Tips

• Always try to standardize the numeric predictors (see `Standardize`). Standardization makes predictors insensitive to the scales on which they are measured.

## Algorithms

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### Training Solver

`fitrnet` uses a limited-memory Broyden-Flecter-Goldfarb-Shanno quasi-Newton algorithm (LBFGS) [3] as its loss function minimization technique, where the software minimizes the mean squared error (MSE).

## References

[1] Glorot, Xavier, and Yoshua Bengio. “Understanding the difficulty of training deep feedforward neural networks.” In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249–256. 2010.

[2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.” In Proceedings of the IEEE international conference on computer vision, pp. 1026–1034. 2015.

[3] Nocedal, J. and S. J. Wright. Numerical Optimization, 2nd ed., New York: Springer, 2006.

Introduced in R2021a