神经网络

Neural networks for binary and multiclass classification

Neural network models are structured as a series of layers that reflect the way the brain processes information. The neural network classifiers 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 neural network classification model, use theClassification Learner应用程序。For greater flexibility, train a neural network classifier usingfitcnetin the command-line interface. After training, you can classify new data by passing the model and the new predictor data to预测.

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

Classification Learner

Train models to classify data using supervised machine learning

Functions

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Create Neural Network Model

`fitcnet`	Train neural network classification model
`compact`	Reduce size of machine learning model

Interpret Prediction

`酸橙`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	沙普利价值es

Cross-Validate

`crossval`	Cross-validate machine learning model
`kfoldLoss`	Classification loss for cross-validated classification model
`kfoldPredict`	Classify observations in cross-validated classification model
`kfoldEdge`	Classification edge for cross-validated classification model
`kfoldmargin`	交叉验证分类模型的分类边缘
`kfoldfun`	Cross-validate function for classification

测量性能

`loss`	Classification loss for neural network classifier
`resubLoss`	Resubstitution classification loss
`edge`	Classification edge for neural network classifier
`margin`	Classification margins for neural network classifier
`resubEdge`	Resubstitution classification edge
`resubMargin`	Resubstitution classification margin

分类观察结果

`预测`	使用神经网络分类器对观察进行分类
`resubPredict`	Classify training data using trained classifier

Objects

`ClassificationNeuralNetwork`	Neural network model for classification
`CompactClassificationNeuralNetwork`	Compact neural network model for classification
`ClassificationPartitionedModel`	Cross-validated classification model

Topics

Assess Neural Network Classifier Performance
Usefitcnetto create a feedforward neural network classifier with fully connected layers, and assess the performance of the model on test data.
使用分类学习者应用程序培训神经网络分类器
Create and compare neural network classifiers, and export trained models to make predictions for new data.