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Neural Networks

Neural networks for regression

Neural network models are structured as a series of layers that reflect the way the brain processes information. The regression neural network models available in Statistics and Machine Learning Toolbox™ are fully connected, feedforward neural networks for which you can adjust the size of the fully connected layers and change the activation functions of the layers.

To train a regression neural network model, use the Regression Learner app. For greater flexibility, train a regression neural network model using fitrnet in the command-line interface. After training, you can predict responses for new data by passing the model and the new predictor data to predict.

If you want to create more complex deep learning networks and have Deep Learning Toolbox™, you can try the Deep Network Designer (Deep Learning Toolbox) app.

Apps

Regression Learner

Train regression models to predict data using supervised machine learning

Blocks

RegressionNeuralNetwork Predict

Predict responses using neural network regression model (Since R2021b)

Functions

expand all

Create Neural Network Model

`fitrnet`	Train neural network regression model (Since R2021a)
`compact`	Reduce size of machine learning model

Interpret Prediction

`lime`	Local interpretable model-agnostic explanations (LIME) (Since R2020b)
`partialDependence`	Compute partial dependence (Since R2020b)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values (Since R2021a)

Cross-Validate

`crossval`	Cross-validate machine learning model
`kfoldLoss`	Loss for cross-validated partitioned regression model
`kfoldPredict`	Predict responses for observations in cross-validated regression model
`kfoldfun`	Cross-validate function for regression

Measure Performance

`loss`	Loss for regression neural network (Since R2021a)
`resubLoss`	Resubstitution regression loss

Predict Responses

`predict`	Predict responses using regression neural network (Since R2021a)
`resubPredict`	Predict responses for training data using trained regression model

Objects

`RegressionNeuralNetwork`	Neural network model for regression (Since R2021a)
`CompactRegressionNeuralNetwork`	Compact neural network model for regression (Since R2021a)
`RegressionPartitionedNeuralNetwork`	Cross-validated regression neural network model (Since R2023b)

Topics

Assess Regression Neural Network Performance
Use fitrnet to create a feedforward regression neural network model with fully connected layers, and assess the performance of the model on test data.
Train Regression Neural Networks Using Regression Learner App
Create and compare regression neural networks, and export trained models to make predictions for new data.
Deploy Neural Network Regression Model to FPGA/ASIC Platform
Predict in Simulink^® using a neural network regression model, and deploy the Simulink model to an FPGA/ASIC platform by using HDL code generation.