RProp MLP - Data Preparation Regression - Apply

The input can be data with strings, integers and doubles. It should have a string variable called "Target" as a regression (numeric value) column.

This component applies preparations data to be used for the RProp MLP Learner" or "MultiLayerPerceptron Predictor" or "" node creating a neural network. It will:

• 01 Remove missing values

• 02 Convert categories to numbers

• convert integers to double (MLP seems to like that)

• 03 Normalize the double columns

• 04 Normalization of the Target column (later needed separately for De-Normalization)

The conversions come from PMML Files from the Component:

"RProp MLP - Data Preparation Regression - Compute"

https://hub.knime.com/s/ffkDVugfu2chRvo5

Input ports

1. Type: Table
   original data
   the original data with String "Target" 0/1
2. Type: PMML
   01 Missing Values (PMML)
   PMML setting the missing values
3. Type: PMML
   02 Category to Number (PMML)
   PMML converting the strings to numbers (double later)
4. Type: PMML
   03 Normalization (PMML)
   PMML for the Normalization
5. Type: PMML
   04 Normalization of Target (PMML)
   No description available
6. Type: Table
   Column Name
   Table Specs of the modified training data

Output ports

1. Type: Table
   Normalized without Target
   No description available
2. Type: Table
   Normalized WITH Target
   No description available

External resources

• Hub: Heart Disease - Machine Learning Case - MLP - Parameter Optimization Loop
• Component: RProp MLP - Data Preparation Binary - Compute
• Node: MultiLayerPerceptron Predictor
• Node: RProp MLP Learner