Neural network models

BernoulliRBM/MLPClassifier/MLPRegressor

BernoulliRBM

Input Data

	Inputs
*Target Data(X)	Data Input
*Model Name	Name of the user-defined model
Transform	If not empty, the result matrix will be stored here. If left empty, the model will only compute the bias and weights.
Conv.DataFrame	If checked, the output data created by Transform will be converted into a DataFrame.
*x	User-selected columns to be X. One or more numeric columns should be selected.

[Task information of BernoulliRBM]

MLPClassifier

Input Data

	Inputs
*Target Data(X)	Data Input
*Model Name	Name of the user-defined model
Predict Output	If not empty, the model will additionally make prediction on Target Data and store the result here.
Merge Data	Predict Output will be created with predicted labels concatenated to Merge Data.
*x	User-selected columns to be X. One or more numeric columns should be selected.
*y	User-selected columns to be Y. Only one column should be selected.

[Task information of MLPClassifier]

MLPRegressor

Input Data

	Inputs
*Target Data(X)	Data Input
*Model Name	Name of the user-defined model
Predict Output	If not empty, the model will additionally make prediction on Target Data and store the result here.
Merge Data	Predict Output will be created with predicted labels concatenated to Merge Data.
*x	User-selected columns to be X. One or more numeric columns should be selected.
*y	User-selected columns to be Y. Only one numeric column should be selected.

[Task information of MLPRegressor]

Workflow Example

Python Package

BernoulliRBM: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.BernoulliRBM.html

MLPClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html

MLPRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html