sklearn Boston Dataset-based Neural Networks Regression Evaluation Task

An sklearn Boston Dataset-based Neural Networks Regression Evaluation Task is a sklearn Boston dataset evaluation task (on sklearn's Boston dataset) restricted to Artificial Neural Network Training Systems.

• Example(s):
  • Training RMSE on the data, [1]:
    • ~5.332 for sklearn.neural_network.MLPRegressor(activation='relu', solver='lbfgs'), a MLP Training System using a ReLU as an activation function.
    • ~7.3161 for sklearn.neural_network.MLPRegressor(activation='logistic'), a MLP Training System using a Sigmoid Activation Function.
    • ~6.3860 for sklearn.neural_network.MLPRegressor(activation='tanh', solver='lbfgs'), a MLP Training System using a Hyperbolic Tangent Activation Function.
  • 10-fold RMSE on the data, [2]:
    • ~6.7892 for sklearn.neural_network.MLPRegressor(activation='relu', solver='lbfgs'), a MLP Training System using a ReLU as an activation function.
    • ~8.0986 for sklearn.neural_network.MLPRegressor(activation='logistic'), a MLP Training System using a Sigmoid Activation Function.
    • ~8.0147 for sklearn.neural_network.MLPRegressor(activation='tanh', solver='lbfgs'), a MLP Training System using a Hyperbolic Tangent Activation Function.
  • …
• Counter-Example(s):
  • A sklearn.linear_model.
  • A sklearn.neighbors.
• See: sklearn.ensemble Module, sklearn Boston Dataset Evaluation, sklearn Boston Dataset-based Regression Trees Evaluation Task.

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