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"NeuralNetwork" (Machine Learning Method)

  • Method for Classify and Predict.
  • Models class probabilities or predicts the value distribution using a neural network.

Details & Suboptions

  • A neural network consists of stacked layers, each performing a simple computation. Information is processed layer by layer from the input layer to the output layer. The neural network is trained to minimize a loss function on the training set using gradient descent.
  • The following options can be given:
  • MaxTrainingRounds Automaticmaximum number of iterations over the dataset
    "NetworkDepth" Automaticthe depth of the network
  • The option "NetworkDepth" controls the capacity of the network. A deeper network will be able to fit more complex patterns but will be more prone to overfitting.
  • The option MaxTrainingRounds can be used to speed up the training but also as a regularization parameter: setting a lower value can prevent overfitting.

Examples

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Basic Examples  (2)

Train a classifier function on labeled examples:

Obtain information about the classifier:

Classify a new example:

Generate some data and visualize it:

Train a predictor function on it:

Compare the data with the predicted values and look at the standard deviation:

Options  (2)

MaxTrainingRounds  (1)

Generate a training set and visualize it:

Train two predictors using different MaxTrainingRounds and compare their performances on the training set:

"NetworkDepth"  (1)

Use the "NetworkDepth" suboption to specify the number of units in the neural network:

Train a second PredictorFunction by changing the "NetworkDepth":

Plot the mean prediction: