gmm properties

Last updated: Jan 18, 2024

gmm properties

Gaussian Mixture node icon A Gaussian Mixture© model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. One can think of mixture models as generalizing k-means clustering to incorporate information about the covariance structure of the data as well as the centers of the latent Gaussians. The Gaussian Mixture node in SPSS Modeler exposes the core features and commonly used parameters of the Gaussian Mixture library. The node is implemented in Python.

Table 1. gmm properties
`gmm` properties	Data type	Property description
`custom_fields`	boolean	This option tells the node to use field information specified here instead of that given in any upstream Type node(s). After selecting this option, specify the following fields as required.
`inputs`	field	List of the field names for input.
`target`	field	One field name for target.
`fast_build`	boolean	Utilize multiple CPU cores to improve model building.
`use_partition`	boolean	Set to `True` or `False` to specify whether to use partitioned data. Default is `False`.
`covariance_type`	string	Specify `Full`, `Tied`, `Diag`, or `Spherical` to set the covariance type.
`number_component`	integer	Specify an integer for the number of mixture components. Minimum value is `1`. Default value is `2`.
`component_lable`	boolean	Specify `True` to set the cluster label to a string or `False` to set the cluster label to a number. Default is `False`.
`label_prefix`	string	If using a string cluster label, you can specify a prefix.
`enable_random_seed`	boolean	Specify `True` if you want to use a random seed. Default is `False`.
`random_seed`	integer	If using a random seed, specify an integer to be used for generating random samples.
`tol`	Double	Specify the convergence threshold. Default is `0.000.1`.
`max_iter`	integer	Specify the maximum number of iterations to perform. Default is `100`.
`init_params`	string	Set the initialization parameter to use. Options are `Kmeans` or `Random`.
`warm_start`	boolean	Specify `True` to use the solution of the last fitting as the initialization for the next call of fit. Default is `False`.