MTLEARN Procedure
AUTOTUNE Statement
AUTOTUNE <options>;
The AUTOTUNE statement searches for the best combination of values of the MAXITER=, REGL1=, REGL2=, and TOLERANCE= options in the PROC MTLEARN statement.
Table 2 summarizes the options that you can specify in the AUTOTUNE statement. For more information about all options except the TUNINGPARAMETERS= option, see the option’s description in the section AUTOTUNE Statement in Chapter 2, Shared Concepts. The TUNINGPARAMETERS= option is described following Table 2.
Table 2: AUTOTUNE Statement Options
| Option | Description |
|---|---|
| APPENDLOOKUP | Specifies that the table specified in the HISTORYTABLE= option contain the rows from the table specified in the LOOKUPTABLE= option |
| CONSTRAINTS= | Specifies the constraints on the output metrics |
| EVALHISTORY= | Specifies how to report the evaluation history of the tuner |
| FOLDCOLUMN= | Specifies the column in the data table in which the cross validation fold value is indicated |
| FRACTION= | Specifies the fraction of observations to use for validation |
| HISTORYTABLE= | Specifies the CAS table that contains the evaluation history |
| KFOLD= | Specifies the number of folds for k-fold cross validation |
| LIVEUPDATE | Specifies that the table specified in the HISTORYTABLE= option be updated at every evaluation |
| LOOKUPTABLE= | Specifies the CAS table to use for evaluation lookup |
| MAXBAYES= | Specifies the maximum number of points in the kriging model |
| MAXEVALS= | Specifies the maximum number of evaluations |
| MAXITER= | Specifies the maximum number of iterations when SEARCHMETHOD=GA or SEARCHMETHOD=BAYESIAN |
| MAXTIME= | Specifies the maximum time for all iterations |
| MAXTRAINTIME= | Specifies the maximum time for a model training |
| NCONVITER= | Specifies the number of convergence iterations |
| NOGRIDSHUFFLE | Requests that the grid points not be shuffled |
| NOLOCALSEARCH | Disables local search optimization |
| NPARALLEL= | Specifies the number of parallel sessions |
| NSUBSESSIONWORKERS= | Specifies the number of workers in parallel sessions |
| OBJECTIVE= | Specifies the objective function |
| POPSIZE= | Specifies the population size when SEARCHMETHOD=GA or SEARCHMETHOD=BAYESIAN |
| SAMPLESIZE= | Specifies the sample size when SEARCHMETHOD=LHS or SEARCHMETHOD=RANDOM |
| SEARCHMETHOD= | Specifies the search method that the optimizer uses |
| SECONDOBJECTIVE= | Specifies the second objective to use for tuning |
| SELECTINITPOINT | Specifies that the tuner select the best evaluation from the lookup table |
| TARGETEVENT= | Specifies the target event for ROC-based calculations |
| TRAINFRACTION= | Specifies the fraction of observations to use for training |
| TUNINGPARAMETERS= | Specifies the custom tuning parameters |
| USEPARAMETERS= | Specifies how to handle the TUNINGPARAMETERS= option |
-
TUNINGPARAMETERS=(suboption …<suboption>)
TUNEPARMS=(suboption …<suboption>) -
specifies which parameters to tune and which ranges to tune over. If USEPARAMETERS=STANDARD, this option is ignored.
You can specify one or more of the following suboptions:
- MAXITER (LB=number UB=number VALUES=value-list INIT=number EXCLUDE)
-
specifies information about the maximum number of iterations to use for tuning the multitask learning model. For more information, see the MAXITER= option in the PROC MTLEARN statement.
You can specify the following additional suboptions:
- REGL1 (LB=number UB=number VALUES=value-list INIT=number EXCLUDE)
-
specifies information about the
penalization weight to use for tuning the multitask learning model. For more information, see the REGL1= option in the PROC MTLEARN statement. You can specify the following additional suboptions:
- LB=number
-
specifies the minimum
penalization weight to consider during tuning. If you specify this suboption, you cannot specify the VALUES= suboption. By default, LB=0.
- UB=number
-
specifies the maximum
penalization weight to consider during tuning. If you specify this suboption, you cannot specify the VALUES= suboption. By default, UB=1000.
- VALUES=value-list
specifies a list of values to consider for the
penalization weight during tuning, where value-list is a space-separated list of numbers greater than or equal to 0. If you specify this suboption, you cannot specify either the LB= or UB= suboption. - INIT=number
-
specifies the initial
penalization weight for the tuner to use. By default, INIT=0.01.
- EXCLUDE
excludes the
penalization weight from the tuning process. If you specify this suboption, any specified LB=, UB=, VALUES=, and INIT= suboptions are ignored.
- REGL2 (LB=number UB=number VALUES=value-list INIT=number EXCLUDE)
-
specifies information about the
penalization weight to use for tuning the multitask learning model. For more information, see the REGL2= option in the PROC MTLEARN statement. You can specify the following additional suboptions:
- LB=number
-
specifies the minimum
penalization weight to consider during tuning. If you specify this suboption, you cannot specify the VALUES= suboption. By default, LB=0.
- UB=number
-
specifies the maximum
penalization weight to consider during tuning. If you specify this suboption, you cannot specify the VALUES= suboption. By default, UB=1000.
- VALUES=value-list
specifies a list of values to consider for the
penalization weight during tuning, where value-list is a space-separated list of numbers greater than or equal to 0. If you specify this suboption, you cannot specify either the LB= or UB= suboption. - INIT=number
-
specifies the initial
penalization weight for the tuner to use. By default, INIT=0.01.
- EXCLUDE
excludes the
penalization weight from the tuning process. If you specify this suboption, any specified LB=, UB=, VALUES=, and INIT= suboptions are ignored.
- TOLERANCE (LB=number UB=number VALUES=value-list INIT=number EXCLUDE)
-
specifies information about the optimization tolerance to use for tuning the multitask learning model. For more information, see the TOLERANCE= option in the PROC MTLEARN statement.
You can specify the following additional suboptions: