gmm properties
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.
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. |