Workflow 2¶

• read data
• determine percentage of missing values per attribute
• discard attributes for which percentage of missing values is larger than a user-specified threshold
• fill in missing values for remaining attributes using method appropriate for each attribute, depending on the missing data mechanism
• decide which normalization technique to use
• normalize the data
• construct training and test set using sampling with replacement (out-of-bag estimation)
• determine parameters for ANN (number of hidden layers, number of nodes in hidden layers, etc)
• build model using training set
• evaluate model using test set
• determine which samples predicted incorrectly
• store these samples only for a later investigation
• save model
• discard intermediate datasets