Neural Network based ML
Single Hidden NN, DNN, Stuttgart Neural Network
Last updated
Single Hidden NN, DNN, Stuttgart Neural Network
Last updated
Parameters
Input
*Target Data
Input data
*Model Name
Name of the model (fill out at your discretion)
*Output Name
Name of the output after calculation
Merge Data
If not empty, the prediction data will be stored in the selected data frame(optional)
*X (all data type)
User-selected columns to be X
one or more columns should be selected
*Y
User-selected column to be Y
Only one column should be selected
Parameters
Input
*Target Data
Input data
*Model Name
Name of the model (fill out at your discretion)
*Output Name
Name of the output after calculation
Merge Data
If not empty, the prediction data will be stored in the selected data frame(optional)
*X
User-selected columns to be X
At least one numeric/complex column should be selected
*Y
User-selected column to be Y
Only one column should be selected
Parameters
Input
*Target Data
Input data
*Model Name
Name of the model (fill out at your discretion)
*Output Name
Name of the output after calculation
Merge Data
If not empty, the prediction data will be stored in the selected data frame(optional)
*X (all data type)
User-selected columns to be X
At least one column should be selected
*Y
User-selected column to be Y
Only one column should be selected
Single Hidden NN: https://www.rdocumentation.org/packages/nnet/versions/7.3-14/topics/nnet
DNN: https://www.rdocumentation.org/packages/neuralnet/versions/1.44.2/topics/neuralnet
Multi Layer Perceptron: https://www.rdocumentation.org/packages/RSNNS/versions/0.4-12/topics/mlp
Elman Network: https://rdrr.io/cran/RSNNS/man/elman.html
Jordan Network: https://rdrr.io/cran/RSNNS/man/jordan.html
