Main Content

Neural network model for regression

A `RegressionNeuralNetwork`

object is a trained, feedforward, and
fully connected neural network for regression. The first fully connected layer of the neural
network has a connection from the network input (predictor data `X`

), and each
subsequent layer has a connection from the previous layer. Each fully connected layer
multiplies the input by a weight matrix (`LayerWeights`

) and
then adds a bias vector (`LayerBiases`

). An
activation function follows each fully connected layer, excluding the last (`Activations`

and
`OutputLayerActivation`

). The final fully connected layer produces the network's
output, namely predicted response values. For more information, see Neural Network Structure.

Create a `RegressionNeuralNetwork`

object by using `fitrnet`

.

`compact` | Reduce size of machine learning model |

`crossval` | Cross-validate machine learning model |

`loss` | Loss for regression neural network |

`resubLoss` | Resubstitution regression loss |

`resubPredict` | Predict responses for training data using trained regression model |

`partialDependence` | Compute partial dependence |

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

`predict` | Predict responses using regression neural network |

`CompactRegressionNeuralNetwork`

| `fitrnet`

| `loss`

| `predict`

| `RegressionPartitionedModel`