RProp MLP - Data Preparation Binary - Compute

The input can be data with strings, integers and doubles. It should have a string variable called "Target" as a binary label/target (0/1)

This component prepares data to be used for the "RProp MLP Learner" 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

Also the new column specs will be provided

The conversions will be stored in PMML Files to be later used to prepare new data.

Input ports

1. Type: Table
   the original data
   the original data with String "Target" 0/1

Output ports

1. Type: Table
   Normalized WITH Target
   Normalized original data WITH Target
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: Table
   Column Name
   Table Specs of the modified training data

External resources

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