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


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


  • 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.


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.


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