Last updated: Feb 11, 2025
The Synthetic Minority Over-sampling Technique (SMOTE) node provides an
over-sampling 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
imbalanced-learn© 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. |