LIGHTGRADBOOST Procedure
PROC LIGHTGRADBOOST Statement
PROC LIGHTGRADBOOST <options>;
The PROC LIGHTGRADBOOST statement invokes the procedure. Table 2 summarizes the options in this statement.
Table 2: PROC LIGHTGRADBOOST Statement Options
| Option | Description |
|---|---|
| Basic Options | |
| BAGGINGFRACTION= | Randomly selects a portion of the observations without resampling |
| BAGGINGFREQUENCY= | Specifies the frequency of bagging |
| BOOSTING= | Specifies the type of boosting |
| DATA= | Specifies the input data table |
| DETERMINISTIC | Ensures stable results when the same data and same parameters are used |
| INPUTFRACTION= | Randomly selects a subset of features on each iteration or tree. If you set it to 0.8, the LightGBM algorithm selects 80% of features before training each tree. |
| LASSO= | Specifies the L1-norm regularization parameter |
| LEAFSIZE= | Specifies the minimum number of observations per leaf |
| LEARNINGRATE= | Specifies the learning rate for each tree |
| MAXCATEGORIES= | Limits the number of split points considered for categorical features |
| MAXDEPTH= | Limits the maximum depth of the tree model. A value less than or equal to 0 means no limit. |
| MAXITERS= | Specifies the number of iterations for the boosting |
| MONOTONELIST= | Specifies constraints of monotone features |
| NTHREADS= | Specifies the number of threads to use in the computation |
| NUMBIN= | Specifies the number of bins to use for numeric variables in calculating the decision tree |
| OBJECTIVE= | Specifies the type of objective |
| RIDGE= | Specifies the L2-norm regularization parameter |
| SEED= | Specifies the random number seed to use for model building |
| USEMISSING | Specifies the use of special handling of missing values |
| VALIDATIONDATA= | Specifies the validation data table |
You can specify the following options:
- BAGGINGFRACTION=number
-
randomly selects a portion of the observations without resampling. If number is nonzero, the LightGBM algorithm enables bagging.
By default, BAGGINGFRACTION=1.0. You can tune this value by using the AUTOTUNE statement.
- BAGGINGFREQUENCY=number
-
specifies the frequency of bagging. If number is greater than 0, the LightGBM algorithm performs bagging. If number is n, the LightGBM algorithm randomly selects a value (
) to use for the next n iterations. By default, BAGGINGFREQUENCY=0. You can tune this value by using the AUTOTUNE statement.
- BOOSTING=DART | GBDT | GOSS | RF
-
specifies the type of boosting to use. You can specify the following values:
- DART
uses dropouts meet multiple additive regression trees.
- GBDT
uses the traditional gradient boosting decision tree.
- GOSS
uses gradient-based one-side sampling.
- RF
uses the random forest. You must specify the BAGGINGFRACTION= and BAGGINGFREQUENCY= options in order to use this boosting type.
By default, BOOSTING=GBDT.
- DATA=libref.data-table
-
names the input data table for PROC LIGHTGRADBOOST to use. The default is the most recently created data table. libref.data-table is a two-level name, where
- libref
refers to a collection of information that is defined in the LIBNAME statement and includes the
library, which includes a path to the data. For more information about libref, see the section Using SAS Viya Workbench.- data-table
specifies the name of the input data table.
- DETERMINISTIC
ensures stable results when you use the same data and the same parameters.
-
INPUTFRACTION=number
FEATUREFRACTION=number -
randomly selects a subset of features on each iteration or tree. If number is 0.8, the LightGBM algorithm selects 80% of features before training each tree.
By default, INPUTFRACTION=1.0.
-
LEAFSIZE=number
MINLEAFSIZE=number -
specifies the minimum number of observations that each child of a split must contain in the training data table in order for the split to be considered.
By default, LEAFSIZE=20. You can tune this value by using the AUTOTUNE statement.
- LEARNINGRATE=number
-
specifies the learning rate for each tree, where number must be greater than 0.
By default, LEARNINGRATE=0.1. You can tune this value by using the AUTOTUNE statement.
- MAXCATEGORIES=number
limits the number of split points to be considered for categorical features. By default, MAXCATEGORIES=32. This value must be greater than 0.
- MAXDEPTH=number
-
limits the maximum depth of the tree model. A number less than or equal to 0 means no limit.
You can tune this value by using the AUTOTUNE statement.
-
MAXITERS=number
NUMITERS=number -
specifies the maximum number of iterations for the boosting.
By default, MAXITERS=100. You can tune this value by using the AUTOTUNE statement.
-
MONOTONELIST=(–1 0 1)
MONOTONECONSTRAINTS=(–1 0 1) specifies series of constraints of monotone features, where 1 represents increasing constraints, –1 represents decreasing constraints, and 0 represents no constraints.
- NTHREADS=number
specifies the number of threads to use in the computation. The default number of threads is calculated for best performance according to the data.
-
NUMBIN=number
NBINS=number
INTERVALBINS=number -
specifies the number of bins to use for binning the interval input variables. PROC LIGHTGRADBOOST bins continuous predictors to a fixed bin size. This option controls the number of bins and thus also the size of the bins.
By default, NUMBIN=255. You can tune this value by using the AUTOTUNE statement.
- OBJECTIVE=function
-
specifies the objective function to use. You can specify the following values:
- BINARY
uses binary classification.
- CROSS_ENTROPY
uses cross-entropy.
- CROSS_ENTROPY_LAMBDA
uses an alternative parameterization of cross-entropy.
- FAIR
uses fair loss.
- GAMMA
uses gamma regression.
- HUBER
uses Huber regression.
- MAPE
uses mean absolute percentage error regression.
- MULTICLASS
uses multiclass classification.
- MULTICLASSOVA
uses one-versus-all multiclass classification.
- POISSON
uses Poisson regression.
- QUANTILE
uses quantile regression.
- REGRESSION
uses regression.
- REGRESSION_L1
uses L1 regression.
- TWEEDIE
uses Tweedie regression.
By default, OBJECTIVE=REGRESSION.
-
REGL1=number
L1=number
LASSO=number -
specifies the L1-norm regularization parameter, where number must be nonnegative.
By default, REGL1=0. You can tune this value by using the AUTOTUNE statement.
-
REGL2=number
L2=number
RIDGE=number -
specifies the L2-norm regularization parameter on prediction. The value of number must be nonnegative.
By default, REGL2=1. You can tune this value by using the AUTOTUNE statement.
- SEED=number
specifies the initial seed for random number generation for model building.
- USEMISSING
specifies the use of special handling of missing values. You can disable this option by omitting it. The LightGBM model uses not a number (NaN) values to represent missing values by default.
-
VALIDATIONDATA=data
VALIDDATA=data specifies that validation data be used. You can use this option to avoid overfitting the training data, combined with parameters such as early stopping rounds.