data_subset_table#

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The Data Subset Table#

data_subset_id#

This column has type integer and is the primary key for this table. Its initial value is zero, and it increments by one for each row.

data_id#

This column has type integer and is a data_id in the data table. These values are unique and monotone increasing with data_subset_id . This column identifies which rows of the data_table are included in the fit_data_subset_table . This is defined by the set of rows in the data table such that the following conditions hold:

Node#

The node is the Parent Node or a Descendant of the parent node.

Covariates#

All of the Covariates satisfy the max_difference criteria.

hold_out#

This column has type integer . If the value in this column is zero (one), the corresponding data is (is not) included in subsequent fits. The init_command initializes this column as zero. The hold_out_command can be used to randomly choose non-zero entries in this column. This column is set to zero by the init_command .

density_id#

This column has type integer . It specifies which density is used for the corresponding data. The init_command set it to be the same as the corresponding density_id in the data table.

eta#

This column has type real . It specifies the value of eta used for the corresponding data. The init_command set it to be the same as the corresponding eta in the data table.

sample_size#

This column has type integer . It specifies the value of sample_size used for the corresponding data. The init_command set it to be the same as the corresponding sample_size in the data table.

nu#

This column has type real . It specifies the value of nu used for the corresponding data. The init_command set it to be the same as the corresponding nu in the data table.

Example#

See init_command.py example and test.