A multi-layer perceptron (MLP) algorithm with backpropagation.

Inputs

• Data: input dataset
• Preprocessor: preprocessing method(s)

Outputs

• Learner: multi-layer perceptron learning algorithm
• Model: trained model

The Neural Network widget uses sklearn’s Multi-layer Perceptron algorithm that can learn non-linear models as well as linear.

1. A name under which it will appear in other widgets. The default name is “Neural Network”.
2. Set model parameters:
   • Neurons per hidden layer: defined as the ith element represents the number of neurons in the ith hidden layer. E.g. a neural network with 3 layers can be defined as 2, 3, 2.
   • Activation function for the hidden layer:
     • Identity: no-op activation, useful to implement linear bottleneck
     • Logistic: the logistic sigmoid function
     • tanh: the hyperbolic tan function
     • ReLu: the rectified linear unit function
   • Solver for weight optimization:
     • L-BFGS-B: an optimizer in the family of quasi-Newton methods
     • SGD: stochastic gradient descent
     • Adam: stochastic gradient-based optimizer
   • Alpha: L2 penalty (regularization term) parameter
   • Max iterations: maximum number of iterations

Other parameters are set to sklearn’s defaults. 3. Produce a report. 4. When the box is ticked (Apply Automatically), the widget will communicate changes automatically. Alternatively, click Apply.

Examples

The first example is a classification task on iris dataset. We compare the results of Neural Network with the Logistic Regression.

The second example is a prediction task, still using the iris data. This workflow shows how to use the Learner output. We input the Neural Network prediction model into Predictions and observe the predicted values.