Editing Column Details

When you create a new predictive model, you can edit properties and specify descriptive information for a column, to ensure that the columns are identified in the right category for the predictive model generation.
Note
any updates you make in the dataset are permanent: if you (or another user) reuse this dataset in another predictive model or another predictive scenario, the changes will remain.
The following column properties can be updated:
Field Values
Description:

User specified column name. This can be a more relevant title as opposed to the "Name" column that contains the dataset column name.

 Text field
Storage:

Data type for the column.
  • String
  • Integer Note that a telephone or account numbers should not be considered numbers.
  • Number
  • Boolean
  • Date
  • Date and Time
  • Time
  • Angle
Type:

Statistical data type

Continuous: columns whose values are numerical, continuous, and sortable. They can be used to calculate aggregations (such as min, median or max).

Nominal: columns that label data. They have no quantitative value (such as 1 and 2 to indicate male or female).

Ordinal: discrete numeric column where the relative order is important.

Textual: text column containing phrases, sentences, or complete text.

Tip
While creating a predictive model, if the column you want to select as target or entity (time series forecasting) isn't available it is likely that this column wasn't assigned the right data type, you can correct this here in Edit Column Details.
Missing:

String specified here replaces a missing value in the column.

For example, if you enter #Empty, then any rows with no entries will receive #Empty as a value.
Key

Specify one or multiple unique identifiers for observations in the dataset.

 Your dataset needs to have at least one key column if you use a regression predictive model.