smotenode properties

Last updated: Jan 18, 2024

SMOTE node icon 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.

Table 1. smotenode properties
`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`.