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 theRegression Learner应用程序。For greater flexibility, train a regression neural network model usingfitrnetin the command-line interface. After training, you can predict responses for new data by passing the model and the new predictor data topredict.

If you want to create more complex deep learning networks and have Deep Learning Toolbox™, you can try theDeep Network Designer(Deep Learning Toolbox)应用程序。

Apps

Regression Learner

Train regression models to predict data using supervised machine learning

Functions

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Create Neural Network Model

`fitrnet`	Train neural network regression model
`compact`	Reduce size of machine learning model

Interpret Prediction

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

Cross-Validate

`crossval`	Cross-validate machine learning model
`kfoldLoss`	Loss for cross-validated partitioned regression model
`kfoldPredict`	预测反应×v的观察alidated regression model
`kfoldfun`	Cross-validate function for regression

Measure Performance

`loss`	Loss for regression neural network
`resubLoss`	Resubstitution regression loss

Predict Responses

`predict`	Predict responses using regression neural network
`resubPredict`	Predict responses for training data using trained regression model

Objects

`RegressionNeuralNetwork`	Neural network model for regression
`CompactRegressionNeuralNetwork`	Compact neural network model for regression
`RegressionPartitionedModel`	Cross-validated regression model

Topics

Assess Regression Neural Network Performance
Usefitrnetto 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.