ocsvmnode properties
The One-Class SVM node uses an unsupervised learning
algorithm. The node can be used for novelty detection. It will detect the soft boundary of a given
set of samples, to then classify new points as belonging to that set or not. This One-Class SVM
modeling node in SPSS Modeler is implemented in Python and requires the
scikit-learn© Python library.
ocsvmnode 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. |
role_use |
string | Specify predefined to use predefined roles or custom to use
custom field assignments. Default is predefined. |
splits
|
field | List of the field names for split. |
use_partition
|
Boolean | Specify true or false. Default is true. If
set to true, only training data will be used when building the model. |
mode_type
|
string | The mode. Possible values are simple or expert. All
parameters on the Expert tab will be disabled if simple is specified. |
stopping_criteria
|
string | A string of scientific notation. Possible values are 1.0E-1,
1.0E-2, 1.0E-3, 1.0E-4, 1.0E-5,
or 1.0E-6. Default is 1.0E-3. |
precision
|
float | The regression precision (nu). Bound on the fraction of training errors and support vectors.
Specify a number greater than 0 and less than or equal to 1.0.
Default is 0.1. |
kernel
|
string | The kernel type to use in the algorithm. Possible values are linear,
poly, rbf, sigmoid, or
precomputed. Default is rbf. |
enable_gamma
|
Boolean | Enables the gamma parameter. Specify true or
false. Default is true. |
gamma
|
float | This parameter is only enabled for the kernels rbf, poly,
and sigmoid. If the enable_gamma parameter is set to
false, this parameter will be set to auto. If set to
true, the default is 0.1. |
coef0
|
float | Independent term in the kernel function. This parameter is only enabled for the
poly kernel and the sigmoid kernel. Default value is
0.0. |
degree
|
integer | Degree of the polynomial kernel function. This parameter is only enabled for the
poly kernel. Specify any integer. Default is 3. |
shrinking
|
Boolean | Specifies whether to use the shrinking heuristic option. Specify true or
false. Default is false. |
enable_cache_size
|
Boolean | Enables the cache_size parameter. Specify true or
false. Default is false. |
cache_size
|
float | The size of the kernel cache in MB. Default is 200. |
enable_random_seed |
Boolean | Enables the random_seed parameter. Specify true or
false. Default is false. |
random_seed |
integer | The random number seed to use when shuffling data for probability estimation. Specify any integer. |
pc_type |
string | The type of the parallel coordinates graphic. Possible options are
independent or general. |
lines_amount |
integer | Maximum number of lines to include on the graphic. Specify an integer between
1 and 1000. |
lines_fields_custom |
Boolean | Enables the lines_fields parameter, which allows you to specify custom
fields to show in the graph output. If set to false, all fields will be shown. If
set to true, only the fields specified with the lines_fields parameter will be
shown. For performance reasons, a maximum of 20 fields will be displayed. |
lines_fields |
field | List of the field names to include on the graphic as vertical axes. |
enable_graphic |
Boolean | Specify true or false. Enables graphic output (disable this
option if you want to save time and reduce stream file size). |
enable_hpo |
Boolean | Specify true or false to enable or disable the HPO options.
If set to true, Rbfopt will be applied to find out the "best" One-Class SVM model
automatically, which reaches the target objective value defined by the user with the following
target_objval parameter. |
target_objval |
float | The objective function value (error rate of the model on the samples) we want to reach (for
example, the value of the unknown optimum). Set this parameter to the appropriate value if the
optimum is unknown (for example, 0.01). |
max_iterations |
integer | Maximum number of iterations for trying the model. Default is 1000. |
max_evaluations |
integer | Maximum number of function evaluations for trying the model, where the focus is accuracy over
speed. Default is 300. |