Neural Network
A multilayer perceptron (MLP) algorithm with backpropagation.
Inputs
 Data: input dataset
 Preprocessor: preprocessing method(s)
Outputs
 Learner: multilayer perceptron learning algorithm
 Model: trained model
The Neural Network widget uses sklearn's Multilayer Perceptron algorithm that can learn nonlinear models as well as linear.

A name under which it will appear in other widgets. The default name is "Neural Network".

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: noop 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:
 LBFGSB: an optimizer in the family of quasiNewton methods
 SGD: stochastic gradient descent
 Adam: stochastic gradientbased optimizer
 Alpha: L2 penalty (regularization term) parameter
 Max iterations: maximum number of iterations
Other parameters are set to sklearn's defaults.

Produce a report.

When the box is ticked (Apply Automatically), the widget will communicate changes automatically. Alternatively, click Apply.
Preprocessing
Neural Network uses default preprocessing when no other preprocessors are given. It executes them in the following order:
 removes instances with unknown target values
 continuizes categorical variables (with onehotencoding)
 removes empty columns
 imputes missing values with mean values
 normalizes the data by centering to mean and scaling to standard deviation of 1
To remove default preprocessing, connect an empty Preprocess widget to the learner.
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.