Support Vector Machine Classifier.
This is a simple interface to the libSVM package.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Methods
clone() | Create full copy of the classifier. |
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_sensitivity_analyzer(**kwargs) | Returns an appropriate SensitivityAnalyzer. |
get_space() | Query the processing space name of this node. |
is_trained([dataset]) | Either classifier was already trained. |
predict(obj, data, *args, **kwargs) | |
repredict(obj, data, *args, **kwargs) | |
reset() | |
retrain(dataset, **kwargs) | Helper to avoid check if data was changed actually changed |
set_postproc(node) | Assigns a post-processing node |
set_space(name) | Set the processing space name of this node. |
summary() | Provide quick summary over the SVM classifier |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |
Interface class to LIBSVM classifiers and regressions.
Default implementation (C/nu/epsilon SVM) is chosen depending on the given parameters (C/nu/tube_epsilon).
SVM/SVR definition is dependent on specifying kernel, implementation type, and parameters for each of them which vary depending on the choices made.
Desired implementation is specified in svm_impl argument. Here is the list if implementations known to this class, along with specific to them parameters (described below among the rest of parameters), and what tasks it is capable to deal with (e.g. regression, binary and/or multiclass classification):
ONE_CLASS : one-class-SVM Capabilities: oneclass C_SVC : C-SVM classification
Parameters: C Capabilities: binary, multiclass
Kernel choice is specified as a kernel instance with kwargument kernel. Some kernels (e.g. Linear) might allow computation of per feature sensitivity.
Parameters : | tube_epsilon :
C :
weight :
probability :
epsilon :
weight_label :
shrinking :
nu :
kernel :
enable_ca : None or list of str
disable_ca : None or list of str
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
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Methods
clone() | Create full copy of the classifier. |
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_sensitivity_analyzer(**kwargs) | Returns an appropriate SensitivityAnalyzer. |
get_space() | Query the processing space name of this node. |
is_trained([dataset]) | Either classifier was already trained. |
predict(obj, data, *args, **kwargs) | |
repredict(obj, data, *args, **kwargs) | |
reset() | |
retrain(dataset, **kwargs) | Helper to avoid check if data was changed actually changed |
set_postproc(node) | Assigns a post-processing node |
set_space(name) | Set the processing space name of this node. |
summary() | Provide quick summary over the SVM classifier |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |
Access to the SVM model.
Provide quick summary over the SVM classifier