"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 Automatic maximum number of iterations over the dataset "NetworkDepth" Automatic the 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.
Examplesopen allclose all
Basic Examples (2)
Generate a training set and visualize it:
Train two predictors using different MaxTrainingRounds and compare their performances on the training set:
Use the "NetworkDepth" suboption to specify the number of units in the neural network:
Train a second PredictorFunction by changing the "NetworkDepth":