CALL td_analyze (
'linearscore',
'required_parameter_list [ optional_parameter; [...] ]'
);
- required_parameter_list
database = input_database_name;
tablename = input_table_name;
modeldatabase = model_database_name;
modeltablename = model_table_name;
- optional_parameter
{ gensqlonly = { true | false } |
outputdatabase = output_database_name |
outputtablename = output_table_name |
index = column_name [,...] |
overwrite = { true | false } |
predicted = column_name |
residual = residual_column_name |
retain = column_name [,...] |
samplescoresize = sample_score_size |
scoringmethod = { score | evaluate | scoreandevaluate }
}
Syntax Elements
- database
- The database containing the input table.
- tablename
- The input table to score.
- modeldatabase
- The database containing the model input table.
- modeltablename
- The input table containing the linear model to use in scoring, output by the Linear Regression function.
- gensqlonly
- [Optional] True returns the SQL for the function as a result set but does not run it.
- False runs the SQL for the function but does not return it as a result set.
- Default: false
- outputdatabase
- [Optional] The database that contains the output score table.
- outputtablename
- [Optional] The name of the score output table containing key columns and predicted values of the dependent variable in the linear model and any columns specified by groupby, residual, and retained.
- If you do not specify both outputdatabase and outputtablename, the function returns a result set instead of any output table.
- index
- [Optional] The columns for the primary index of the score output table. These columns must form a unique key for the score output table. Otherwise, a given observation has more than one score.
- These index columns are included both in the Primary Index clause and the select list.
- Default: Primary index columns of the input table
- overwrite
- [Optional] Whether to drop the output tables before creating new ones.
- Default: true
- predicted
- [Optional] The input table column that has the predicted value. Unnecessary if scoringmethod=evaluate.
- Default: dependent
- residual
- [Optional] If scoringmethod=scoreandevaluate, the name of the input table column that has the residual value (the difference between the predicted and actual value of the dependent variable).
- Default: Residual
- retain
- [Optional] One or more input table columns to be copy to the output table.
- samplescoresize
- [Optional] If scoringmethod=score or scoringmethod=scoreandevaluate, the number of output table rows to show in a sample of the result set (an integer).
- Default behavior: Function returns no sample.
- scoringmethod
- [Optional] Whether to score (only), evaluate (only), or score and evaluate.
- With the options evaluate and scoreandevaluate, the function outputs a confusion matrix table in XML format. The table includes counts of predicted and actual values of the dependent variable of the decision tree model and counts of correct and incorrect predictions.
- Default: score