smotenode properties
The Synthetic Minority Oversampling Technique (SMOTE) node provides an oversampling algorithm to deal with imbalanced data sets. It provides an advanced method for balancing data. The SMOTE process node in SPSS Modeler is implemented in Python and requires the imbalancedlearn© Python library.
smotenode properties 
Data type  Property description 

target 
field  The target field. 
sample_ratio

string  Enables a custom ratio value. The two options are Auto (sample_ratio_auto )
or Set ratio (sample_ratio_manual ). 
sample_ratio_value

float  The ratio is the number of samples in the minority class over the number of samples in the
majority class. It must be larger than 0 and less than or equal to
1 . Default is auto . 
enable_random_seed 
Boolean  If set to true , the random_seed property will be
enabled. 
random_seed

integer  The seed used by the random number generator. 
k_neighbours

integer  The number of nearest neighbors to be used for constructing synthetic samples. Default is
5 . 
m_neighbours

integer  The number of nearest neighbors to be used for determining if a minority sample is in danger.
This option is only enabled with the SMOTE algorithm types borderline1 and
borderline2 . Default is 10 . 
algorithm 
string  The type of SMOTE algorithm: regular , borderline1 , or
borderline2 . 
use_partition

Boolean  If set to true , only training data will be used for model building. Default
is true . 