sklearn.neural network.MLPRegressor

A sklearn.neural_network.MLPRegressor is a multi-layer perceptron regression system within sklearn.neural_network.

• Context
  • Usage:

1) Import MLP Regression System from scikit-learn : from sklearn.neural_network import MLPRegressor

2) Create design matrix X and response vector Y

3) Create Regressor object: regressor_model=MLPRegressor([hidden_layer_sizes=(100, ), activation=’relu’, solver=’adam’, alpha=0.0001, batch_size=’auto’, learning_rate=’constant’, learning_rate_init=0.001, ...])

4) Choose method(s):

• fit(X, y) Fits the regression model to data matrix X and target(s) y.
• get_params([deep]) Gets parameters for this estimator.
• predict(X) Predicts using the multi-layer perceptron model.
• score(X, y[, sample_weight]) Returns the coefficient of determination R^2 of the prediction.
• set_params(**params) Set the parameters of this estimator.

• Example(s):
  • MLP_regression_10foldcv_boston.py (Boston Dataset-based Regression)

Method: MLP using ReLU RMSE on the data: 5.3323 RMSE on 10-fold CV: 6.7892	Method: MLP using Logistic Neurons RMSE on the data: 7.3161 RMSE on 10-fold CV: 8.0986	Method: MLP using TanH Neurons RMSE on the data: 6.3860 RMSE on 10-fold CV: 8.0147

• Counter-Example(s):
  • sklearn.neural network.MLPClassifier
  • sklearn.neural_network.BernoulliRBM
  • sklearn.svm.LinearSVC
  • sklearn.svm.LinearSVR
• See: Artificial Neural Network, Supervised Learning System, Regression Task, Feedforward Neural Network, Restricted Boltzmann Machines, Support Vector Machines.

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References

2017a

• (Scikit-Learn, 2017A) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html Retrieved:2017-12-17
  • QUOTE: class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(100, ), activation=’relu’, solver=’adam’, alpha=0.0001, batch_size=’auto’, learning_rate=’constant’, learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08)

    Multi-layer Perceptron regressor. This model optimizes the squared-loss using LBFGS or stochastic gradient descent.

    (...)

    Notes

    MLPRegressor trains iteratively since at each time step the partial derivatives of the loss function with respect to the model parameters are computed to update the parameters. It can also have a regularization term added to the loss function that shrinks model parameters to prevent overfitting. This implementation works with data represented as dense and sparse numpy arrays of floating point values.

2017b

• (sklearn,2017) ⇒ http://scikit-learn.org/stable/modules/neural_networks_supervised.html#regression Retrieved:2017-12-3.
  • QUOTE: Class MLPRegressor implements a multi-layer perceptron (MLP) that trains using backpropagation with no activation function in the output layer, which can also be seen as using the identity function as activation function. Therefore, it uses the square error as the loss function, and the output is a set of continuous values.

    MLPRegressor also supports multi-output regression, in which a sample can have more than one target.

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