sklearn Boston Dataset-based Neural Networks Regression Evaluation Task
(Redirected from sklearn Boston Dataset-based Neural Networks Regression Evaluation)
Jump to navigation
Jump to search
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
forsklearn.neural_network.MLPRegressor(activation='relu', solver='lbfgs')
, a MLP Training System using a ReLU as an activation function. - ~
7.3161
forsklearn.neural_network.MLPRegressor(activation='logistic')
, a MLP Training System using a Sigmoid Activation Function. - ~
6.3860
forsklearn.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
forsklearn.neural_network.MLPRegressor(activation='relu', solver='lbfgs')
, a MLP Training System using a ReLU as an activation function. - ~
8.0986
forsklearn.neural_network.MLPRegressor(activation='logistic')
, a MLP Training System using a Sigmoid Activation Function. - ~
8.0147
forsklearn.neural_network.MLPRegressor(activation='tanh', solver='lbfgs')
, a MLP Training System using a Hyperbolic Tangent Activation Function.
- ~
- …
- Training RMSE on the data, [1]:
- Counter-Example(s):
- See: sklearn.ensemble Module, sklearn Boston Dataset Evaluation, sklearn Boston Dataset-based Regression Trees Evaluation Task.