IDAX.MSE - Calculate the mean squared error of regression predictions

Use this stored procedure to calculate the mean squared error of regression predictions. For the calculation, the predictions that are made when a regression model is applied on data are compared to the real values of this data.

Authorization

The privileges held by the authorization ID of the statement must include the IDAX_USER role.

Syntax

IDAX.MSE(in parameter_string varchar(32672))

Parameter descriptions

parameter_string

Mandatory one-string parameter that contains pairs of <parameter>=<value> entries that are separated by a comma.

Data type: VARCHAR(32672)

The following list shows the parameter values:

intable: Mandatory.; The name or view of the input table that is used to test the quality of the model.; Data type: VARCHAR(256)
id: Mandatory.; The column of the input table that identifies a unique instance ID.; Data type: VARCHAR(128)
target: Mandatory.; The column of the input table that identifies the values that are to be predicted.; Data type: VARCHAR(128)
resulttable: Mandatory.; The name of the input table that contains the values that are predicted for the <intable>.; Data type: VARCHAR(256)
resultid: Optional.; The column of the <resulttable> that identifies a unique instance ID.; Default: "ID"; Data type: VARCHAR(128)
resulttarget: Optional.; The column of the <resulttable> that contains the predicted values.; Default: "CLASS"; Data type: VARCHAR(128)

Returned information

DOUBLE the mean squared error as a result set.

Example

CALL IDAX.SPLIT_DATA('intable=samples.customer_churn,traintable=cust_train,testtable=cust_test,id=cust_id,fraction=0.30');
CALL IDAX.GROW_REGTREE('model=cust_rt, intable=cust_train, id=cust_id, target=duration,  minsplit=2');
CALL IDAX.PREDICT_REGTREE('model=cust_rt, intable=cust_test, outtable=cust_rt_out');
CALL IDAX.MSE('intable=cust_test,id=cust_id,target=duration,resulttable=cust_rt_out');